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Use of mobile technology is widespread, particularly among the younger generation. There is a huge potential for utilizing such technology in lecture classes with large numbers of students, serving as an interaction tool between the students and lecturers. The challenge is to identify significant adoption factors to ensure effective adoption of mobile technology to promote interactivity between students and lecturers. This paper aims to examine factors supporting the use of mobile wireless technology during lectures to promote interactivity between students and lecturers in Malaysia's higher education institutions. A survey involving higher education students enrolled in on-campus academic courses in Malaysia was conducted with a sample size of 302. Factor analysis results identified five factors: independent variables System Usefulness (SU), User System Perception (USP), User Uncertainty Avoidance (UUA), System and Information Quality (SIQ), and dependent variable Mobile Wireless Technology Adoption for Interactive Lectures (MWT_AIL). All independent variables are positively associated to MWT_AIL, with UUA and SIQ having higher levels of significance compared to SU and USP. Respondents were selected from higher learning institutions from urban areas in Malaysia. Therefore results obtained are not representative of the entire higher education landscape in Malaysia, and future studies are warranted to include higher learning institutions located in rural areas. It is hoped that findings from this study will serve as a catalyst for future studies to be conducted, particularly among higher education researchers seeking ways to utilize technology effectively to enhance the learning experiences of students. [ABSTRACT FROM AUTHOR]

In this article, we are reporting the findings of a usability study of opinion mining systems' visualizations. The objectives of this study are to first to rank the visualizations of the opinion mining systems and second to identify important visualization metrics. A questionnaire survey was designed to ask users their level of agreement or disagreement about the 11 selected visualizations against a set of information visualization metrics on a Likert scale. The data were collected by conducting seminars and using a web-based online questionnaire (N = 146). The collected data were analyzed using descriptive statistics and an independent sample t-test to rank the visualizations and investigate differences between perceptions of the two groups of respondents (the participants of the seminars and the online questionnaire), respectively. The results revealed that simple, eye pleasing, easy to understand, user-friendly visualizations with less pre-knowledge required rated higher than others. It is concluded that the participants of the online questionnaire mostly required more pre-knowledge to comprehend the visualizations as compared to the participants of the seminars. The important information visualization metrics are eye pleasing, easy to understand, user-friendly, informative design, usefulness, and representation style. The results of this study could aid in the design and development of visualizations for opinion mining systems. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the moderating effect of health professional's working experience on the relationship between factors of Health Information System Security Policies Compliance Behaviour (HISSPC) model. A survey (i.e., n = 454) was conducted to test the differences between high experience and low experience health professionals who were Health Information System (HIS) users. The HISSPC model was tested using partial least squares (PLS) approach with results indicating the coefficient of determination (i.e., R2) for high experience group (i.e., 63 percent)to be slightly higher than the low experience group (i.e., 60 percent). Statistical differences were noted for the relationship between management support and user's compliance behaviour in both groups,with stronger relationship forlow experience HIS users compared to high experience HIS users. In contrast, perceived susceptibility was found to significantly influence highly experienced users to comply with HIS security policies, however it had no significant effect for the low experience group.The overall moderating effect size for high experience users was approximately 0.07 (i.e. small) and no moderating effect was observed for the low experience group (i.e., ƒ2 = 0.01). It was believed that the findings will provide better guidelines to fellow researchers and policy makers in improving information security behaviour among health professionals in hospitals, particularly those with varying working experiences. [ABSTRACT FROM AUTHOR]

The paper first explores the factors that affect the use of social networks to enhance teaching and learning experiences among students and lecturers, using structured questionnaires prepared based on the Push–Pull–Mooring framework. A total of 455 students and lecturers from higher learning institutions in Malaysia participated in this study. Multiple linear regressions revealed five factors: E-Learning Perception, Academic Reasons, Ease of Use, Convenience and Social Networking to be positively associated with Teaching and Learning Benefits obtained from the use of social networks. These factors were then used to develop Book2U – a social e-learning tool which integrates the popular features of social networks. User testing revealed Book2U was well received by the majority of the respondents. It is believed that teaching and learning activities in educational sectors can be improved by using social network and e-learning-integrated tools, such as Book2U. [ABSTRACT FROM AUTHOR]

This study investigated the factors that might affect the use of social media in e-learning, particularly targeting the use of Facebook, Twitter and YouTube. Questionnaire design was based on Push-Pull-Mooring framework, and distributed to students and academicians in local universities (N = 455). Five significant predictors were found, viz. E-learning Perception, Convenience, Academic Reasons, Ease of Use and Social Networking. A further analysis revealed younger respondents were more enthusiastic in using social media in e-learning, particularly for Academic Reasons, Social Networking and Ease of Use. The findings indicate that students and academicians are open to the idea of using social media in e-learning, more apparent among the younger ones. It is believed that integration of social media in e-learning will enhance communication and collaboration among students and academicians. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced vector space model, Multi-Term Adjacency Keyword-Order Model, to improve the relevancy of search results, specifically document search. Our model is based on the concept of keyword grouping. The keyword-order relationship in the adjacency terms is taken into consideration in measuring a term's weight. Assigning more weights to adjacency terms in a query order results in the document vector being moved closer to the query vector, and hence increases the relevancy between the two vectors and thus eventually results in documents with better relevancy being retrieved. The performance of our model is measured based on precision metrics against the performance of a classic vector space model and the performance of a Multi-Term Vector Space Model. Results show that our model performs better in retrieving more relevant results based on a particular search query compared to both the other models. [ABSTRACT FROM AUTHOR]

Conversational systems are gaining popularity rapidly. Consequently, the believability of the conversational systems or chatterbots is becoming increasingly important. Recent research has proven that learning chatterbots tend to be rated as being more believable by users. Based on Raj's Model for Chatterbot Trust, we present a model for allowing chatterbots to determine the degree of contradictions in contradictory statements when learning thereby allowing them to potentially learn more accurately via a form of discourse. Some information that is learnt by a chatterbot may be contradicted by other information presented subsequently. Choosing correctly which information to use is critical in chatterbot believability. Our model uses sentence structures and patterns to compute contradiction degrees that can be used to overcome the limitations of Raj's Trust Model, which takes any contradictory information as being equally contradictory as opposed to some contradictions being greater than others and therefore having a greater impact on the actions that the chatterbot should take. This paper also presents the relevant proofs and tests of the contradiction degree model as well as a potential implementation method to integrate our model with Raj's Trust Model. [ABSTRACT FROM AUTHOR]

This article reports findings from a study in which a total of 110 youths were interviewed to analyze the overall pattern of Short Message Service (SMS) usage among Malaysians. Structured interview questionnaires were used to collect the required data. The selected respondents were between 17 and 25 years old, an age category that has proven to represent the most active SMS users worldwide. Descriptive statistics including frequencies and mean values were used to analyze the data. The results indicate that Malaysian youths are as active SMS users as youths in other countries, with the majority sending between five to seven messages daily. As for gender differences, females are found to be more actively involved in SMS activities compared to the males. The findings should prove interesting for those examining the use of SMS among the younger users, especially in an Asian country such as Malaysia. [ABSTRACT FROM AUTHOR]

Hand anthropometry and gender influences on Short Message Service (SMS) usage satisfaction were investigated using structured questionnaire interviews with 110 participants. The collected data were then filtered, resulting in a total of 73 participants. Hand-size, thumb length, circumference and mobile phone dimensions were recorded. Focus was on keypad design factors, which include: key size, shape, layout, texture, simplicity and space between keys. Females were found to be more satisfied with the key size and layout than males. Significant differences were noted among subjects with different hand-size with respect to key size and space between keys. One of the recommended improvements is to have larger keys with more space between them for those with large hand-size. The results obtained can be used by mobile phone designers to design customized mobile phones; for example, mobile phones that suit users with larger hands and thumbs, especially males. [ABSTRACT FROM AUTHOR]

This study investigates the underlying motives for online fake news sharing during the COVID-19 pandemic, an unprecedented time that witnessed a spike in the spread of false content. Motives were identified based on a fake news sharing model developed using the SocioCultural-Psychological-Technology (SCulPT) model, Uses and Gratification (U&G) theory and Self-Determination Theory (SDT), and further extended using fake news predictors/gratifications from past studies. A self-administered survey resulted in 869 online Malaysian respondents aged between 18 and 59 years old (Mean = 22.6, Standard deviation = 6.13). Structured equation modelling revealed the fake news sharing model to collectively account for 49.2 % of the variance, with Altruism (β = 0.333; p < 0.001), Ignorance (β = 0.165; p < 0.001) and Entertainment (β = 0.139; p < 0.001) significantly predicting the behaviour. Conversely, Availability/Effort, Pass Time and Fear of Missing Out were found to be insignificant. Our findings indicate that fake news sharing behavior is determined by different motives, hence these need to be understood in order to develop better solutions to mitigate this problem. • Study identified underlying motives for fake news sharing during COVID-19 pandemic. • Six motives explain 49.2 % of fake news sharing behavior. • Altruism and Ignorance had significant moderate effect on fake news sharing. • Entertainment had a significant weak effect on fake news sharing. • Availability/Effort, Pass Time and Fear of Missing Out were insignificant motives. [ABSTRACT FROM AUTHOR]

The paper investigated the relationship between readiness towards a cashless society and adoption of cashless applications among Malaysians. Specifically, drivers and inhibitors were explored and examined to assess their direct and indirect effects through the development of the Cashless Society Readiness-Adoption model. The model was developed based on Unified Theory of Acceptance and Use of Technology 2 and Technology Readiness Index 2.0. Self-administered surveys were administered in online and offline modes, resulting in 258 valid responses (M age = 34.5; SD age = 7.82). Structured equation modelling revealed Ease of Use, Usefulness, Innovativeness, Optimism and Lack of awareness to directly affect users' readiness in going cashless. Interestingly, perceived readiness had no direct effect in the adoption of digital payment services, however, the effect was found to be significant with the inclusion of risk and intrinsic motivation as the mediating factors. Similarities and dissimilarities with existing studies are discussed in this paper. Results would be beneficial to respective parties such as service providers, financial institutions and government agencies to develop and design strategies to encourage more people to embrace digital payment services effectively. • Development of Cashless Society Readiness-Adoption model. • Questionnaire surveys among 18–64 year old Malaysians (N = 258). • Being cashless ready does not impact digital payment adoption. • Risk is a major concern affecting digital payment adoption. • Users adopt digital payment for intrinsic purposes. [ABSTRACT FROM AUTHOR]

Abstract An adaptive learning environment provides personalised information to the learner through self-directed study. An adaptive learning environment model can be subdivided into a learner model, domain model, instructional model and adaptive engine. Personal traits comprise part of the components in a learner model and can be identified either explicitly or implicitly in an adaptive learning environment. In such an environment, the e-learning system should adapt to a learner's needs. However, even though academic research on adaptive learning environments has increased, the field lacks a comprehensive literature analysis of learners' personal traits in these environments. This study conducts a systematic literature review to identify the most commonly used personal traits in modelling the learner and the existing techniques suitable for identifying personal traits in an adaptive learning environment. A total of 140 articles spanning the years 2010–2017 are initially reviewed, from which 78 are selected based on the inclusion and exclusion criteria relevant to this study. This study provides an overview of learners' personal traits and the techniques used to identify them to provide a basis for improving adaptive learning environments. The findings indicate that most of the previous works used a learning style from the cognition learning domain category to model individual personal traits, while the computer-based detection technique was commonly applied to identify a learner's personal traits in adaptive learning environments. This study reveals the common learner characteristics used to develop learner models and the techniques for implementing such models. The findings of this paper can guide other researchers to recognise various personal traits and the identification technique for further studies, as well as assist developers in the development of the adaptive learning system. Highlights • Comprehensive literature on identification of learner's characteristics. • The correlation between a specific personal trait and learning object. • Learner model implementation and enhancement techniques. • Impact of integrating personal traits in progressive learning. [ABSTRACT FROM AUTHOR]

Purpose The effort in addition to relevance is a major factor for satisfaction and utility of the document to the actual user. The purpose of this paper is to propose a method in generating relevance judgments that incorporate effort without human judges' involvement. Then the study determines the variation in system rankings due to low effort relevance judgment in evaluating retrieval systems at different depth of evaluation.Design/methodology/approach Effort-based relevance judgments are generated using a proposed boxplot approach for simple document features, HTML features and readability features. The boxplot approach is a simple yet repeatable approach in classifying documents' effort while ensuring outlier scores do not skew the grading of the entire set of documents.Findings The retrieval systems evaluation using low effort relevance judgments has a stronger influence on shallow depth of evaluation compared to deeper depth. It is proved that difference in the system rankings is due to low effort documents and not the number of relevant documents.Originality/value Hence, it is crucial to evaluate retrieval systems at shallow depth using low effort relevance judgments. [ABSTRACT FROM AUTHOR]

Background: Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings., Objective: This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health., Methods: In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks., Results: The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024., Conclusions: Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users., International Registered Report Identifier (irrid): PRR1-10.2196/54349., (©Zhao Ni, Mary L Peng, Vimala Balakrishnan, Vincent Tee, Iskandar Azwa, Rumana Saifi, LaRon E Nelson, David Vlahov, Frederick L Altice. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 15.02.2024.)

Background: The HIV epidemic continues to grow fastest among men who have sex with men (MSM) in Malaysia in the presence of stigma and discrimination. Engaging MSM on the internet using chatbots supported through artificial intelligence (AI) can potentially help HIV prevention efforts. We previously identified the benefits, limitations, and preferred features of HIV prevention AI chatbots and developed an AI chatbot prototype that is now tested for feasibility and acceptability., Objective: This study aims to test the feasibility and acceptability of an AI chatbot in promoting the uptake of HIV testing and pre-exposure prophylaxis (PrEP) in MSM., Methods: We conducted beta testing with 14 MSM from February to April 2022 using Zoom (Zoom Video Communications, Inc). Beta testing involved 3 steps: a 45-minute human-chatbot interaction using the think-aloud method, a 35-minute semistructured interview, and a 10-minute web-based survey. The first 2 steps were recorded, transcribed verbatim, and analyzed using the Unified Theory of Acceptance and Use of Technology. Emerging themes from the qualitative data were mapped on the 4 domains of the Unified Theory of Acceptance and Use of Technology: performance expectancy, effort expectancy, facilitating conditions, and social influence., Results: Most participants (13/14, 93%) perceived the chatbot to be useful because it provided comprehensive information on HIV testing and PrEP (performance expectancy). All participants indicated that the chatbot was easy to use because of its simple, straightforward design and quick, friendly responses (effort expectancy). Moreover, 93% (13/14) of the participants rated the overall chatbot quality as high, and all participants perceived the chatbot as a helpful tool and would refer it to others. Approximately 79% (11/14) of the participants agreed they would continue using the chatbot. They suggested adding a local language (ie, Bahasa Malaysia) to customize the chatbot to the Malaysian context (facilitating condition) and suggested that the chatbot should also incorporate more information on mental health, HIV risk assessment, and consequences of HIV. In terms of social influence, all participants perceived the chatbot as helpful in avoiding stigma-inducing interactions and thus could increase the frequency of HIV testing and PrEP uptake among MSM., Conclusions: The current AI chatbot is feasible and acceptable to promote the uptake of HIV testing and PrEP. To ensure the successful implementation and dissemination of AI chatbots in Malaysia, they should be customized to communicate in Bahasa Malaysia and upgraded to provide other HIV-related information to improve usability, such as mental health support, risk assessment for sexually transmitted infections, AIDS treatment, and the consequences of contracting HIV., (©Min Hui Cheah, Yan Nee Gan, Frederick L Altice, Jeffrey A Wickersham, Roman Shrestha, Nur Afiqah Mohd Salleh, Kee Seong Ng, Iskandar Azwa, Vimala Balakrishnan, Adeeba Kamarulzaman, Zhao Ni. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 26.01.2024.)

Objective: This study aimed to conduct a scoping review to collect current literature on the knowledge, awareness, and perception (KAP) of sexually transmitted infections/diseases (STIs/STDs) among women in Asia., Methodology: The PRISMA-Scoping methodology was used in this study to extract papers from four databases published between 2018 and 2022. Sixty-eight articles were included after screening and elimination., Results: The studies on KAP of STIs/STDs among women were largely undertaken in Southeast Asia (Indonesia, Malaysia, and Vietnam) and South Asia (India, Pakistan, and Bangladesh). Regardless of the specific cohort of women studied, research indicates consistently low levels of knowledge and awareness across Asia. This trend seems to be more prevalent among female commercial sex workers, women with lower educational levels, and those in poorer socioeconomic positions. In South Asia, cultural, sociological, economic, and gender inequalities, particularly male domination, all have a significant impact on KAP levels., Conclusion: As education is a major factor that influences health behaviour, this review underscores the need to allocate more resources to educational initiatives, particularly targeting vulnerable groups such as sex workers, transgender women, pregnant women, and rural housewives. This strategic focus may contribute significantly to preventing STIs/STDs, particularly in less developed regions/countries.

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models., Competing Interests: Declaration of competing interest The authors declare no conflict of interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This scoping review synthesizes literature to examine the extent of research focusing on knowledge, awareness, perceptions, attitudes, and risky behaviors related to sexually transmitted infections (STIs) in Southeast Asia (SEA). The PRISMA-Scoping approach was adopted targeting articles published from 2018 to 2022, sought from CINALH, PubMed, Web of Science and Scopus. A process of screening and elimination resulted in a total of 70 articles reviewed. Most of the studies were conducted in Indonesia, Thailand, Vietnam, and Malaysia, with the majority focusing on HIV/AIDS. In general, studies examining knowledge, awareness, and risky behaviors related to STIs in SEA reported low levels across various cohorts. However, evidence suggests that these issues are more prominent among individuals with low levels of education or low socioeconomic status, those living in rural areas or those working in the sex/industrial sectors. Engaging in unsafe sex and having multiple partners are the key examples for risky sexual behavior, while fear of being rejected/discriminated/stigmatized and lacking STI awareness were identified as social risky behaviors in SEA. Overall, cultural, societal, economic and gender inequality (male dominance) greatly impact people's knowledge, awareness, perceptions, attitudes, and risky behaviors in SEA. Education is an important factor influencing healthy behavior; therefore, this scoping review calls for increased investment in educating vulnerable populations to prevent STIs, particularly in less-developed countries/regions of SEA.

We present a benchmark comparison of several deep learning models including Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short Term Memory, assessed based on various word embedding approaches, including the Bi-directional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was administered using the Easy Data Augmentation approach resulting in two datasets (original versus augmented). All the models were assessed in two setups, namely 5-class versus 3-class (i.e., compressed version). Findings show the best prediction models were Neural Network-based using Word2Vec, with CNN-RNN-Bi-LSTM producing the highest accuracy (96%) and F -score (91.1%). Individually, RNN was the best model with an accuracy of 87.5% and F -score of 83.5%, while RoBERTa had the best F -score of 73.1%. The study shows that deep learning is better for analyzing the sentiments within the text compared to supervised machine learning and provides a direction for future work and research., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.)

Background: Health information systems are innovative products designed to improve the delivery of effective healthcare, but they are also vulnerable to breaches of information security, including unauthorised access, use, disclosure, disruption, modification or destruction, and duplication of passwords. Greater openness and multi-connectedness between heterogeneous stakeholders within health networks increase the security risk., Objective: The focus of this research was on the indirect effects of management support (MS) on user compliance behaviour (UCB) towards information security policies (ISPs) among health professionals in selected Malaysian public hospitals. The aim was to identify significant factors and provide a clearer understanding of the nature of compliance behaviour in the health sector environment., Method: Using a survey design and stratified random sampling method, self-administered questionnaires were distributed to 454 healthcare professionals in three hospitals. Drawing on theories of planned behaviour, perceived behavioural control (self-efficacy (SE) and MS components) and the trust factor, an information system security policies compliance model was developed to test three related constructs (MS, SE and perceived trust (PT)) and their relationship to UCB towards ISPs., Results: Results showed a 52.8% variation in UCB through significant factors. Partial least squares structural equation modelling demonstrated that all factors were significant and that MS had an indirect effect on UCB through both PT and SE among respondents to this study., Conclusion: The research model based on the theory of planned behaviour in combination with other human and organisational factors has made a useful contribution towards explaining compliance behaviour in relation to organisational ISPs, with trust being the most significant factor. In adopting a multidimensional approach to management-user interactions via multidisciplinary concepts and theories to evaluate the association between the integrated management-user values and the nature of compliance towards ISPs among selected health professionals, this study has made a unique contribution to the literature.

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.