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Sentiment analysis is the method to detect stock market behaviour. Various components are functioning their ups and downs because the stock market is unestimated. The trend of sequence gets influenced through factors and non-linear relationship. Both academic and real-life business, the Stock market prediction is an attractive topic. The prediction is carried out by gathering the customer reviews and classifying them as positive reviews or negative reviews. Various statistical and econometric techniques were introduced for stock market prediction using sentimental analysis. Stock market predictions are suffered from errorless through sentiment analysis techniques. Lesser classification accuracy has a direct cause for the reliability of stock market indicators. To improve prediction performance, the existing problems of stock market prediction were reviewed in this paper.

Due to the increasing number of reviews, it has become more important for businesses to analyze their customer's sentiments. This paper presents a framework that uses pre-trained language models such as BERT, XLNet, and Electra to analyze these sentiments. The framework is based on the Sentiment140 dataset which contains over 1.6 million tweets with tags. This collection of sentiments allows us to perform an evaluation of the models' performance. The goal of this paper is to analyze the effectiveness of these models in categorizing and understanding the sentiments in customer reviews. BERT, for instance, has demonstrated exceptional performance in various tasks related to natural language processing. Another model that is transformer-based is XLNet, which adds more capabilities by utilizing permutation-based learning. On the other hand, the new generation of model, known as Electra, focuses on the generator discriminator learning. Through the incorporation of these models, we can leverage the contextual understanding of the sentiments in the customer reviews. In this paper, we thoroughly examine the performance of the different models in the framework for sentiment analysis. We tested their precision, recall, F1-score, and accuracy in identifying and categorizing the sentiments in customer reviews. We also discuss the impact of adjusting the models on the task, as well as the tradeoffs between performance gains and computational resources. The findings of the study provided valuable information on the utilization of pre-trained models for analyzing customer reviews. We analyzed the performance of the different models BERT, XLNet, Electra, and BERT, revealing their weaknesses and strengths. This helps businesses identify the best model for their sentiment analysis needs. The study's findings have contributed to the advancement of sentiment analysis and natural language processing. It offers valuable recommendations that will aid in the future research efforts.

We empirically study whether firms improve their quality based on online customer reviews in a dynamic quality environment. We do so by analyzing the U.S. hotel industry using data from two major online review platforms: Tripadvisor and Expedia. Using management response as a proxy for whether hotels pay attention to consumer reviews and a difference-in-differences strategy, we find that hotels that are more likely to pay attention to reviews increase their ratings more than hotels that do not do so. Moreover, we show that low-rated hotels experience larger gains as they have more margin of improvement than high-rated hotels. We reinforce these findings by using natural language processing algorithms and show that hotels that respond to reviews improve on issues that are frequently mentioned in their reviews. Overall, our results suggest that online reviews provide a useful source of information for firms that can help them improve their quality.

PurposeAs the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.Design/methodology/approachThis paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.FindingsThe performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.Practical implicationsThe proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.Originality/valueThis study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

This paper investigates the key factors that contribute to the success of movies. By using sentiment and survival analysis, this study classified 1,038 movies according to the customer comments and movie characteristics and compared the number of screening days, the primary measure of success of movies, between the groups. Based on the analysis of film reviews (i.e., positive, negative, and neutral), screening days showed significant differences between (1) the positive and neutral groups, negative and neutral groups, (2) the density (|positive—negative comments|) of the positive and negative groups, (3) drama and action, drama and comedy, (4) domestic and foreign films, (5) G-Rated and R-Rated, R-Rated and X-rated films.

The idea of sentimental analysis is getting attention for the last few years. The key challenges in a sentimental analysis are the collection of huge data from the sources, applying appropriate algorithms or techniques, and classifying them into different sentiments. In this fast-spreading internet world, social media provides a platform for individuals to express their sentiments. With the changing ways of things in different areas in our day-to-day life, the way of expressing one's view or opinion has also changed. People tend to express themselves in their regional language or in a way convenient to them. These individual reviews play an important role in decision-making. With the huge amount of data that is obtained on social media, it is of no use if the opinions are not classified based on their sentiments. This paper provides information about the tweets posted by the customer are positive, negative, or neutral. For this the proposed model first scrape the tweets from Twitter by using Twitter APIs, then later by using text blob, the customer reviews are given different sentiment scores and classify them as positive, negative, or neutral by using text classification model. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.

We proposed a new rule-based text analysis method to effectively summarize and transform unstructured user-generated content (online customer reviews) into an analysable form for tourism and hospit...

Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.

With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers’ preferences in a timely manner. With the continuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification methods. However, how to choose a trusted method is a difficult problem for companies, because customer reviews (or opinions) contain a lot of uncertain information and noise. This article reports on a detailed survey of recent opinion mining literature. It also reviews how to extract text features in opinions that may contain noise or uncertainties, how to express knowledge in opinions, and how to classify them. Through this extensive study, this paper discusses open questions and recommends future research directions for building the next generation of opinion mining systems.

The escalation of online shopping trends, social networks, and blogs makes shopping customers to know premier and quality product from customer reviews posted on social networks. Also, the business...

Customers feedback is a valuable asset for businesses, that can be used in order to improve their performance. One of the fastest spreading areas today in computer science - Sentiment Analysis, helps to extract precious information from textual data, in order to identify the feeling of a statement. This research aims to build a classifier to predict customers’ satisfaction, based on Amazon reviews dataset, for different brands of mobile phones. The paper proposes a comparison between four text classification algorithms - Naïve Bayes, Support Vector Machine, Decision Tree and Random Forest, using different feature extraction techniques, such as Bag of words and TF-IDF. In addition, the models are evaluated using accuracy, precision, recall and F-score metrics. Our experiments revealed that Support Vector Machine achieves the best results and is very suitable for classification of the sentiment on product reviews.

This article examines user-generated content (UGC) related to hotels in three different mass tourism destinations (Antalya, Majorca, and Sharm El Sheikh) that offer services with the all-inclusive system (AIS) to comparatively analyses tourists' evaluations and emotions about service components. While the study was designed with the content analysis method, text mining and sentiment analysis were used together. Customer reviews (UGC) of top hotels in three different mass tourism destinations were collected from an on-line travel review site. A total of 3588 English hotel reviews were analysed by the R program. Analysis of the reviews for famous mass tourism destination hotels in the Mediterranean region has also clearly revealed the priority service characteristics (rooms, staff, and food) and dominant emotions for hotels in all destinations in comparison. Moreover, the multiple correspondence analysis results clearly show how the emotions about the services of the hotels in three different regions diverge. Analysis results provide important clues for mass tourism destination hotels working with AIS.

A surge in public opinions mining against various societal topics using publicly available off-the-shelf sentiment analysis tools is evident in recent times. Since sentiment analysis is a domain-dependent problem, and the majority of the tools are built for customer reviews, the suitability of using such existing off-the-the-shelf tools for a societal topic is subject to investigation. None of the existing studies has thoroughly investigated on societal issues. This paper systematically evaluates the performance of 10 popularly used off-the-shelf tools and 17 state-of-the-art machine learning techniques and investigates their strengths and weaknesses using various societal and non-societal topics.

Today, smartphones are being used to manage almost all aspects of our lives, ranging from personal to professional. Different users have different requirements and preferences while selecting a smartphone. There is ‘no one-size fits all’ remedy when it comes to smartphones. Additionally, the availability of a wide variety of smartphones in the market makes it difficult for the user to select the best one. The use of only product ratings to choose the best smartphone is not sufficient because the interpretation of such ratings can be quite vague and ambiguous. In this paper, reviews of products are incorporated into the decision-making process in order to select the best product for a recommendation. The top five different brands of smartphones are considered for a case study. The proposed system, then, analyses the customer reviews of these smartphones from two online platforms, Flipkart and Amazon, using sentiment analysis techniques. Next, it uses a hybrid MCDM approach, where characteristics of AHP and TOPSIS methods are combined to evaluate the best smartphones from a list of five alternatives and recommend the best product. The result shows that brand1 smartphone is considered to be the best smartphone among five smartphones based on four important decision criteria. The result of the proposed system is also validated by manually annotated customer reviews of the smartphone by experts. It shows that recommendation of the best product by the proposed system matches the experts’ ranking. Thus, the proposed system can be a useful decision support tool for the best smartphone recommendation.

In this paper we focus on the hotel sectors and help them process these huge chunks of data in the form of customer reviews and help them derive useful information. The data pre-processing involves the scrapping of reviews from different sites and storing them and also check the correctness of the regular expression of the reviews. Our modelling employed includes three machine learning algorithms namely Naive Bayes, Support vector machine (svm) and Logistic regression. These three models improve the accuracy of the model as well as its robustness. The main idea of using these models are that the reviews are labelled so that the hotel management need not waste loads of time reading all the reviews. Instead the important reviews can be arranged based on their polarity and the important topic discussed in the review can be highlighted. So that it is easy for the management to analyse both the positive as well as the negative reviews. Sentiment polarity is incorporated to arrange the reviews based on the sentiment the review establishes. This paper helps the world to properly analyse the feedbacks and the reviews given by the customers.

The field of sentiment analysis is widely utilized for analyzing the text data and then extracting the sentiment component out of that. The online commercial websites generates a huge amount of textual data via customer’s reviews, comments, feedbacks and tweets every day. Aspect level analysis of this data provides a great help to retailers in better understanding of customer’s expectations and then shaping their policies accordingly. However, a number of algorithms are existing these days to do aspect level sentiment detection on specified domains, but a few consider bipolar words (words which changes polarity according to context) while doing analyses. In this paper, a novel approach has been presented that utilize aspect level sentiment detection, which focuses on the features of the item. The work has been implemented and tested on Amazon customer reviews (crawled data) where aspect terms are identified first for each review. The system performs pre-processing operations like stemming, tokenization, casing, stop-word removal on the dataset to extract meaningful information and finally gives a rank for its classification in negativity or positivity.

With the rapid development of e-commerce, online consumption has become a mainstream form of consumption in recent years. Text sentiment analysis for a large number of customer reviews on the e-commerce platform can dramatically improve the customers' consumption experience. Although the sentiment analysis approaches based on deep neural network can achieve higher accuracy without human-design features compared with traditional sentiment analysis methods, the accuracy still cannot meet the demand and the training suffers from the issues of over-fitting, vanishing gradient, etc. In this paper, a novel sentiment analysis model named MBGCV is designed to alleviate these problems and improve the accuracy, MBGCV employs a multichannel paradigm and integrates Bidirectional Gated Recurrent Unit (BiGRU), Convolutional Neural Network (CNN) and Variational Information Bottleneck (VIB). The multichannel is exploited to extract multi-grained sentiment features. In each channel, BiGRU is utilized to extract context information, and then CNN is adopted to extract local features. Furthermore, the model combines the advantages of VIB and Maxout activation function to overcome shortcomings such as over-fitting, vanishing gradient in existing sentiment analysis models. By using real review datasets, we carry out extensive experiments to demonstrate that our proposed model can achieve superior accuracy and improve the performance of text sentiment analysis.

In this paper, we face the problem of generating reputation for movies, products, hotels, restaurants and services by mining customer reviews expressed in natural language. To the best of our knowledge, previous studies on reputation generation for online entities have primarily examined semantic and sentiment orientation of customer reviews, disregarding other useful information that could be extracted from reviews, such as review helpfulness and review time. Therefore, we propose a new approach that combines review helpfulness, review time, review attached rating and review sentiment orientation for the purpose of generating a single reputation value toward various entities. The contribution of the paper is threefold. First, we design two equations to compute review helpfulness and review time scores, and we fine-tune Bidirectional Encoder Representations from Transformers (BERT) model to predict the review sentiment orientation probability. Second, we design a formula to assign a numerical score to each review. Then, we propose a new formula to compute reputation value toward the target entity (movie, product, hotel, restaurant, service, etc). Finally, we propose a new form to visualize reputation that depicts numerical reputation value, opinion categories, top positive review and top negative review. Experimental results coming from several real-world data sets of miscellaneous domains collected from IMDb, TripAdvisor and Amazon websites show the effectiveness of the proposed method in generating and visualizing reputation compared to three state-of-the-art reputation systems.

Bukalapak is one of the Customer-To Customer (C2C) e-commerce models. This model is the most widely applied and found on e-commerce sites in Indonesia. The Customer-To Customer (C2C) market is currently still dominant in Indonesia's online retail market. Data collected from Euromonitor estimates that the C2C market contributed 3% of the retail market in Indonesia in 2017, while the B2C market contributed 1.7%. One text mining analysis is that sentiment analysis can be applied to companies that issue a product or service and provide services to receive opinions (feedback) from consumers for the product. Sentiment analysis is applied to classify positive, negative, and neutral feedback from consumers so as to speed up and simplify the company's task to review their product deficiencies. The researcher conducted further analysis on Bukalapak user reviews to find out how user comments or opinions were on Bukalapak using the TF-IDF Algorithm method. And it can be concluded that based on customer review reviews in Bukalapak have a good rating or perception of this Vans shoe product. Can be seen from the results of Sentiments, Sentiment Visualization and WordCloud Visualization which shows that positive reviews have a higher frequency of 70%.

Due to the continuous growth of E-Commerce platforms, more and more data is becoming widely available. Almost every online transaction, from buying products on Amazon to renting vacation homes on Airbnb, allows for users to leave feedback in the form of customer reviews. Therefore, there is a need to provide an overview of the status of customer review analysis. This paper systematically reviews research works conducted in the past ten years (2010–2020) to identify how customer reviews are being analyzed and how this analysis can serve the consumers or vendors. We present several commonly used machine learning algorithms, datasets, and numerous applications of customer review analysis to provide insight as well as discuss directions for future research.

In this COVID-19 scenario the majority have an interest in on-line searching. So, many folks order the merchandise depends on the previous reviews. These reviews square measure enjoying necessary role in creating purchase choices. however, in these reviews' spammers might manufacture pretend reviews because of such behavior of spammers clients would I mislead and create the incorrect call to beat this drawback we've to spot the actual one who posed reviews over just once and therefore the admin can delete that review supported the customer review info.

Nowadays, sentiment analysis is a method used to analyze the sentiment of the feedback given by a user in an online document, such as a blog, comment, and review, and classifies it as negative, positive, or neutral. The classification process relies upon the analysis of the polarity features of the natural language text given by users. Polarity analysis has been an important subtask in sentiment analysis; however, detecting correct polarity has been a major issue. Different researchers have utilized different polarity features, such as standard part-of-speech (POS) tags such as adjectives, adverbs, verbs, and nouns. However, there seems to be a lack of research focusing on the subcategories of these tags. The aim of this research was to propose a method that better recognizes the polarity of natural language text by utilizing different polarity features using the standard POS category and the subcategory combinations in order to explore the specific polarity of text. Several experiments were conducted to examine and compare the efficacies of the proposed method in terms of F-measure, recall, and precision using an Amazon dataset. The results showed that JJ + NN + VB + RB + VBP + RP, which is a POS subcategory combination, obtained better accuracy compared to the baseline approaches by 4.4% in terms of F-measure.

PurposeThe purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.Design/methodology/approachThis research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method.FindingsThe results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.Research limitations/implicationsThe proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.Practical implicationsBy combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically.Originality/valueThis study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.

Kansei Engineering (KE) is a user-oriented technology combing customer psychological feelings and engineering for designing and developing products. Conventionally, questionnaire surveys have been ...

Product features can be classified into different categories based on customer opinions. The rapid development of artificial intelligence and machine learning paves the way toward computational analysis of customer reviews for opinion mining. This paper presents an Intelligent Kano framework to extract, quantify, and classify different product features based on customer reviews. The framework is enabled by a novel integration of multiple artificial intelligence and machine learning techniques such as sentiment analysis and anomaly detection. A case study is conducted to validate the framework’s effectiveness. Over 12,000 customer reviews on two coffee machines are analyzed for the classification.

Designing a product service system begins with understanding customer voices. Compared to the traditional methods such as survey, interview, customer review represents a particular kind of big data that contain rich information that is useful for the design of product service system. This paper presents a new framework that integrates a variety of artificial intelligence and machine learning techniques. All proposed operations of the framework can be realized based on the Google Cloud Platform. A case study is conducted to showcase the practical applicability of some key operations such as opinion mining. More than 40,000 customer reviews about the product Kindle White E-reader were analyzed.

Recently, interests in the appliance of deep learning techniques in natural language processing tasks considerably increased. Sentiment analysis is one of the most difficult tasks in natural language processing, mostly in the Persian Language. Thousands of websites, blogs, social networks like Telegram, Instagram and Twitter update, and modify by Persian users around the world that contains millions of contexts. To extract knowledge of these huge amounts of raw data, Deep Learning techniques became increasingly popular but there is a number of challenges that the novel models encounter with them. In this research, a hybrid deep learning-based sentiment analysis model proposed and implemented on customer reviews data of Digikala Online Retailer website. We already applied the classifier based on various deep learning networks and regularization techniques. Finally, by utilizing a hybrid approach, we achieved the best performance of 78.3 of F 1 score on three different classes: positive, negative, and neutral.

Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc. In multilingual communities around the world, a large amount of social media text is characterized by the presence of Code-Switching. Thus, it has become important to build models that can handle code-switched data. However, annotated code-switched data is scarce and there is a need for unsupervised models and algorithms. We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data. We use the power of pre-trained BERT models for initialization and fine-tune them in an unsupervised manner, only using pseudo labels produced by zero-shot transfer. We test our algorithm on multiple code-switched languages and provide a detailed analysis of the learning dynamics of the algorithm with the aim of answering the question - `Does our unsupervised model understand the Code-Switched languages or does it just learn its representations?'. Our unsupervised models compete well with their supervised counterparts, with their performance reaching within 1-7\% (weighted F1 scores) when compared to supervised models trained for a two class problem.

The eatery is a growing market and along with it grows the competition. To stay on the top, one must have satisfied and happy customers and their reviews are significant for a successful business. Nowadays, restaurants need to take customer reviews into account to enhance the customer experience. In this paper, a hybrid methodology is proposed to overcome this problem, faced by the restaurants, using sentimental analysis on the reviews and differentiate the positive and negative aspects of the restaurant. This paper highlights the importance of machine learning algorithms and is used to find patterns in data that help to make wiser decisions and predictions. The sentiment of the reviews are classified into positive and negative, and the score of each sentiment is also measured. The proposed approach gives a classification accuracy of 84.76% which is better than the existing methods.

Light food refers to healthy and nutritious food that has the characteristics of low calorie, low fat, and high fiber. Light food has been favored by the public, especially by the young generation in recent years. Moreover, affected by the COVID-19 epidemic, consumers’ awareness of a healthy diet has been improved to a certain extent. As both take-out and in-place orders for light food are growing rapidly, there are massive customer reviews left on the Meituan platform. However, massive, multi-dimensional unstructured data has not yet been fully explored. This research aims to explore the customers’ focal points and sentiment polarity of the overall comments and to investigate whether there exist differences of these two aspects before and after the COVID-19. A total of 6968 light food customer reviews on the Meituan platform were crawled and finally used for data analysis. This research first conducted the fine-grained sentiment analysis and classification of the light food customer reviews via the SnowNLP technique. In addition, LDA topic modeling was used to analyze positive and negative topics of customer reviews. The experimental results were visualized and the research showed that the SnowNLP technique and LDA topic modeling achieve high performance in extracting the customers’ sentiments and focal points, which provides theoretical and data support for light food businesses to improve customer service. This research contributes to the existing research on LDA modeling and light food customer review analysis. Several practical and feasible suggestions are further provided for managers in the light food industry.

This study was conducted to apply supervised machine learning methods in opinion mining online customer reviews. First, the study automatically collected 39,976 traveler reviews on hotels in Vietnam on Agoda.com website, then conducted the training with machine learning models to find out which model is most compatible with the training dataset and apply this model to forecast opinions for the collected dataset. The results showed that Logistic Regression (LR), Support Vector Machines (SVM) and Neural Network (NN) methods have the best performance in opinion mining in Vietnamese language. This study is valuable as a reference for applications of opinion mining in the field of business.

Continuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process made 2% increase to the model performance on the Turkish Customer Reviews dataset. The model was established using an LSTM network. Our model showed better performance than Machine Learning techniques and achieved an accuracy of 90.59% on the Turkish dataset and an accuracy of 89.02% on the IMDB dataset.

In today’s digital world, advancement in machine learning has changed the traditional perspective towards business analysis. Orthodox business analysts did not consider customer reviews as cost-effective input for analysis because earlier fetching customer reviews was costly. The emergence of the internet turned the whole world upside down. Now, the customer’s sentiment analysis is the new friend of all business analysts. In this paper, we will be doing a comparative study between the two methods. One in which we use usual parameters like average rating, average review counts. In second, we use a text review as a parameter for sentiment classification. The effectiveness of these methods is evaluated, and the optimal model is selected.

The development of e-commerce has created new shopping trends of customers. In online shopping environments, product reviews play a critical role in the choice of customers. Online reviews are additionally valuable for the manufacturers and the vendors by providing easily accessible feedback to them. In this study, a text analysis method is proposed to find the defective features of the products by detecting features with negative opinion tendency in the clustered customer reviews. The output of the proposed model, the extracted defects, may provide a strong source of guidance both for consumers in purchase decisions and for producers in product improvement.

Food delivery services have gained attention and become a top priority in developed cities by reducing travel time and waiting time by offering online food delivery options for a variety of dishes from a wide variety of restaurants. Therefore, customer analytics have been considered in business analysis by enabling businesses to collect and analyse customer feedback to make business decisions to be more advanced in the future. This paper aims to study the techniques used in customer analytics for food delivery services and identify the factors of customers’ reviews for food delivery services especially in social media. A total of 53 papers reviewed, several techniques and algorithms on customer analytics for food delivery services in social media are Lexicon, machine learning, natural language processing (NLP), support vector machine (SVM), and text mining. The paper further analyse the challenges and factors that give impacts to the customers’ reviews for food delivery services. These findings would be appropriate for development and enhancement of food delivery services in future works.

With the ingress of exponential advancement of Internet technologies & social media platforms, there is a potential increase that can be seen in the development of online commercial websites. With time, people started to buy goods from these websites. So, there is also a great increase in selling goods on the Internet. These sites also facilitate their customers to leave their reviews and share their experiences with other users also. These customer reviews help others to make their decision before buying that product. In other words, these reviews help to show the quality of the product. Hence for this process, mining and understanding of the reviews are very important. In this research work, authors aimed to tackle one of the natural language processing (NLP) problems, i.e., sentiment polarity classification. The authors have performed a study to compare the baseline and statistical method (machine learning) for polarity classification. This work is also intended to compare the baseline method and machine learning method, to understand which method is better and more appropriate toward sentiment classification problems with the help of Python programming. The experimental results found to be satisfactory and compared with the existing literature.

With the enormous platforms available in present days, consumers communicate and interconnect online with web users all around the world to share their experiences. Thus, online platform has become a major source of reviews about different entities. People presently travel frequently around the world for different purposes. Seeking good hotels for accommodation is a prime concern. Customer reviews on hotels help future customers to take decisions about their accommodation as well as help hotel owners to rethink about designing customer facilities. However, many online reviews are biased due to different factors. Many hotel owners come up with attractions like referral rewards, coupons, bonus points etc. to the reviewers to motivate them in writing biased reviews. We have worked on US’s 100 hotel and found 952 incentivized reviews out of 19175 reviews, which is 4.96% of total reviews. A categorization on incentivized reviews is performed as well. Furthermore, hotels are distinguished based on real and incentivized reviews found on them. Results are verified using machine learning algorithms. Random Forest, K-Nearest Neighbor and Support Vector Machine are applied as machine learning algorithms to validate the accuracy of our model and their prediction results are compared. Random Forest outperforms with 94.4% prediction accuracy.

Online reviews became popular as people are taking decisions with the help of them. In this context, the purpose of this project is to develop a deep learning based framework that can be used to classify customer reviews into positive or negative. This process is known as sentiment analysis. It is based on the supervised learning mechanisms where a classifier is built with knowledge of training data and then it is used to classify testing data. A prototype application is built to demonstrate proof of the concept. The success of deep learning highly relies on the availability of large-scale training data. A novel deep learning framework for review sentiment classification which employs prevalently available ratings as weak supervision signals. An algorithm by name Deep Learning based Sentiment Analysis (DLSA) is proposed and implemented to achieve this. A deep learning framework is proposed and implemented. A prototype application is built to demonstrate proof of the concept. The empirical study revealed that the proposed system is better than the state of the art.

Online archives have received a great deal of attention in recent years from a person’s view and thoughts as primary platform. Set of circumstances gives rise to increasing interest in methods for automatically collecting and evaluating individual opinions from online documents such as customer reviews, Weblogs and comments on electronically accessible media, emphasis of current studies are mainly on attitude analysis. Interest of human beings is on designing a structure which can categorize feelings of individuals in the form of automated letter. Retrieving and determining beliefs from Web require appropriate mechanism that can be used to acquire and estimate thoughts of the desires of online consumers, which could be useful for economic or marketing research. An aspect of natural language processing (NLP), sentiment analysis (SA) has experienced a growing interest in the past decade. The difficulties and chances of this rising field are likewise talked about prompting our postulation that the analysis of multimodal sentiment has a significant untapped potential.

Education technologies (EdTech) are one of the fastest growing fields of contemporary teaching and main source of innovation that can enable revolutionary change in educational practice. Online customer reviews play a vital role in choosing among the various online platforms. Parents and teachers make use of these reviews to match with their preference. Similarly, the EdTech companies uses this information to improve their customer service. Importing the customer reviews from online websites and sentiment analysis on the reviews by customers as well as companies could be used for decision making to improve the service delivered by EdTech companies. Sentiment analysis is a supervised learning to identify positive, negative and neutral opinions from text which could be used to understand the overall sentiment of customers towards education technologies.

Understanding customer's sentiment (satisfaction or dissatisfaction) is considered as valuable information for both the potential customers and restaurant authority. However, analyzing customer reviews (unstructured texts) one by one is a difficult task and also practically impossible when the number of reviews is enormous. Therefore, it seems conceivable to have a mechanism to analyze customer reviews automatically and provide the necessary information in a precise way. Here, we introduce a Natural Language Processing (NLP) based opinion mining methodology to analyze the customer opinion automatically. For that, first, a captive portal is used to collect customer's reviews. Then, the opinion mining technique is applied to analyze the reviews to explore customer sentiment about food quality, service, environment, etc. A data-driven experiment is conducted to evaluate the proposed methodology. The experiment result showed the effectiveness of the proposed method for retrieving and analyzing customer sentiment.

Sentimental analysis evolved over last few decades is only focused on textual sentimental analysis. Recent development in the Internet has opened the new doors for information exchange and growth for citizens to publicly raise their opinions with serious bottlenecks when it comes to do analysis of these opinions in terms of sentiments of the users. Even urgency to gain a real time understanding of citizens concerns has grown very rapidly. Since, the viral nature of the media which is fast and distributed one, some issues get rapidly distributed and unpredictably become important through this word of mouth opinions expressed online. In this paper, we proposed the audio opinion mining and sentiment analysis of customer products or services reviews which helps to take decision in today’s business world to improve their growth of the business. Customer discrimination and sentiment analysis is performed on customer reviews collected as audio messages on customer products or services.