602 research outputs found

    The Business Impact of Social Media - Sentiment Analysis Approach -

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    ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ์ถ”์ถœ๋œ 7๊ฐœ์˜ ๊ฐ์„ฑ ๋„๋ฉ”์ธ์ด ์ž๋™์ฐจ ์‹œ์žฅ ์ ์œ ์œจ ์˜ˆ์ธก์— ๋Œ€ํ•œ ๊ฐ์„ฑ ๋ถ„์„ ์‹คํ—˜์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ๋กœ์„œ ์ ํ•ฉํ•œ ์ง€์— ๋Œ€ํ•œ ์‹ ๋ขฐ์„ฑ์„ ํ™•์ธํ•˜๊ณ  ๊ณ ๊ฐ๋“ค์˜ ์˜๊ฒฌ์ด ๊ธฐ์—…์˜ ์„ฑ๊ณผ์— ์–ด๋–ป๊ฒŒ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ง€์— ๋Œ€ํ•˜์—ฌ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•œ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด3๋‹จ๊ณ„์— ๊ฑธ์ณ์„œ ์ง„ํ–‰๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ๊ฐ์„ฑ์‚ฌ์ „ ๊ตฌ์ถ•์˜ ๋‹จ๊ณ„๋กœ์„œ 2013๋…„ 1์›” 1์ผ๋ถ€ํ„ฐ 2015๋…„ 12์›” 31์ผ๊นŒ์ง€ ๋ฏธ๊ตญ ๋‚ด 26๊ฐœ์˜ ์ž๋™์ฐจ ์ œ์กฐ ํšŒ์‚ฌ์˜ ๊ณ ๊ฐ์˜ ์†Œ๋ฆฌ (VOC: Voice of the Customer) ์ด 45,447๊ฐœ๋ฅผ ์ž๋™์ฐจ ์ปค๋ฎค๋‹ˆํ‹ฐ๋กœ๋ถ€ํ„ฐ ํฌ๋กค๋ง (crawling)ํ•˜์—ฌ POS (Part-of-Speech) ์ฆ‰ ํ’ˆ์‚ฌ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๋Š” ํƒœ๊น… (tagging)๊ณผ์ •์„ ๊ฑฐ์ณ ๋ถ€์ •์ , ๊ธ์ •์  ๊ฐ์„ฑ์˜ ๋นˆ๋„์ˆ˜๋ฅผ ์ธก์ •ํ•˜์—ฌ ๊ฐ์„ฑ์‚ฌ์ „์„ ๊ตฌ์ถ•ํ•˜์˜€๊ณ , ์ด์— ๋Œ€ํ•œ ๊ทน์„ฑ์„ ์ธก์ •ํ•˜์—ฌ 7๊ฐœ์˜ ๊ฐ์„ฑ๋„๋ฉ”์ธ์„ ๋งŒ๋“ค์—ˆ์Šต๋‹ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์‹ ๋ขฐ์„ฑ ๋ถ„์„์˜ ๋‹จ๊ณ„๋กœ์„œ ์ž๊ธฐ์ƒ๊ด€๊ด€๊ณ„๋ถ„์„ (Auto-correlation Analysis)๊ณผ ์ฃผ์„ฑ๋ถ„๋ถ„์„ (PCA: Principal Component Analysis)์„ ํ†ตํ•ด ๋ฐ์ดํ„ฐ๊ฐ€ ์‹คํ—˜์— ์ ํ•ฉํ•œ์ง€๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์„ธ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” 2๊ฐœ์˜ ์„ ํ˜•ํšŒ๊ท€๋ถ„์„ ๋ชจ๋ธ๋กœ 7๊ฐœ์˜ ๊ฐ์„ฑ์˜์—ญ์ด ๋ฏธ๊ตญ๋‚ด ์ž๋™์ฐจ ์ œ์กฐ ํšŒ์‚ฌ ์ค‘ GM, ํฌ๋“œ, FCA, ํญ์Šค๋ฐ”๊ฒ ๋“ฑ ์ด 4๊ฐœ์˜ ์ž๋™์ฐจ ์ƒ์‚ฐ ๊ธฐ์—…์„ ์„ ์ •ํ•˜์—ฌ ์ด๋“ค ๊ธฐ์—…์˜ ์„ฑ๊ณผ ์ฆ‰, ์ž๋™์ฐจ ์‹œ์žฅ์ ์œ ์œจ์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ๋Š” ์ง€ ์‹คํ—˜ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์šฐ๋ฆฌ๋Š” 4,815๊ฐœ์˜ ๋ถ€์ •์ ์ธ ์–ดํœ˜๋“ค๊ณผ 2,021๊ฐœ์˜ ๊ธ์ •์ ์ธ ๊ฐ์„ฑ์–ดํœ˜๋“ค์„ ์ถ”์ถœํ•˜์—ฌ ๊ฐ์„ฑ์‚ฌ์ „์„ ๊ตฌ์ถ•ํ•˜์˜€์œผ๋ฉฐ, ๊ตฌ์ถ•๋œ ๊ฐ์„ฑ์‚ฌ์ „์„ ๋ฐ”ํƒ•์œผ๋กœ, ์ถ”์ถœ๋˜๊ณ  ๋ถ„๋ฅ˜๋œ ๋ถ€์ •์ ์ด๊ณ  ๊ธ์ •์ ์ธ ์–ดํœ˜๋“ค์„ ์ž๋™์ฐจ ์‚ฐ์—…์— ๊ด€๋ จ๋œ ์–ดํœ˜๋“ค๊ณผ ์กฐํ•ฉํ•˜์˜€๊ณ , ์ž๊ธฐ์ƒ๊ด€๋ถ„์„๊ณผ PCA (์ฃผ์„ฑ๋ถ„ ๋ถ„์„)๋ฅผ ํ†ตํ•ด ๊ฐ์„ฑ์˜ ํŠน์„ฑ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, ์ž๊ธฐ์ƒ๊ด€๋ถ„์„์— ์˜ํ•ด์„œ ๊ฐ์„ฑ ๋ฐ์ดํ„ฐ์— ์–ด๋–ค ์ผ์ •ํ•œ ํŒจํ„ด์ด ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒƒ์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๊ณ , ๊ฐ๊ฐ์˜ ๊ฐ์„ฑ ์˜์—ญ์˜ ๊ฐ์„ฑ์ด ์ž๊ธฐ์ƒ๊ด€์„ฑ์ด ์žˆ์œผ๋ฉฐ, ๊ฐ์„ฑ์˜ ์‹œ๊ณ„์—ด์„ฑ ๋˜ํ•œ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. PCA์— ์˜ํ•œ ๊ฒฐ๊ณผ๋กœ์„œ, 7๊ฐœ ๊ฐ์„ฑ์˜์—ญ์ด ๋ถ€์ •์„ฑ, ๊ธ์ •์„ฑ, ์ค‘๋ฆฝ์„ฑ์„ ์ฃผ์„ฑ๋ถ„์œผ๋กœ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ž๊ธฐ์ƒ๊ด€๋ถ„์„๊ณผ PCA๋ฅผ ํ†ตํ•œ VOC ๊ฐ์„ฑ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์‹ ๋ขฐ์„ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ 2๊ฐœ์˜ ์„ ํ˜•ํšŒ๊ท€๋ถ„์„ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜์—ฌ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋ชจ๋ธ์€ ์ฃผ์„ฑ๋ถ„ ๋ถ„์„์—์„œ ๋ถ€์ •์  ๊ฐ์„ฑ์˜ Sadness, Anger, Fear์™€ ๊ธ์ •์  ๊ฐ์„ฑ๋„๋ฉ”์ธ์ธ Delight, Satisfaction์„ ๋…๋ฆฝ๋ณ€์ˆ˜๋กœ ์„ ์ •ํ•˜๊ณ , ์‹œ์žฅ์ ์œ ์œจ์„ ์ข…์†๋ณ€์ˆ˜๋กœ ์„ ์ •ํ•˜์—ฌ ์‹คํ–‰ํ•˜์˜€๊ณ  ๋‘ ๋ฒˆ์งธ ๋ชจ๋ธ์€ ์ฒซ ๋ฒˆ์งธ ๋ชจ๋ธ์— ์ฃผ์„ฑ๋ถ„์ด ์ค‘๋ฆฝ์„ฑ์œผ๋กœ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์˜จ Shame, Frustration์„ ๋…๋ฆฝ๋ณ€์ˆ˜์— ์ถ”๊ฐ€ํ•˜์—ฌ ์ค‘๋ฆฝ์„ฑ์„ ๋ ๊ณ  ์žˆ๋Š” ๊ฐ์„ฑ์ด ์‹œ์žฅ ์ ์œ ์œจ์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ๋Š” ์ง€๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ฐ ๊ธฐ์—… ๋งˆ๋‹ค ์‹œ์žฅ์ ์œ ์œจ์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฐ์„ฑ๋“ค์ด ์กด์žฌํ•˜๊ณ  ๋ชจ๋ธ 1๊ณผ, ๋ชจ๋ธ 2์—์„œ์˜ ๊ฐ์„ฑ ์˜ํ–ฅ๋ ฅ์ด ์ฐจ์ด๊ฐ€ ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด, ๋ฐ์ดํ„ฐ ์ƒ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋ฅผ ๊ฐ€์ง„ ๊ฐ์„ฑ์ด ๊ณผ๊ฑฐ ๊ฐ’์— ๊ธฐ์ดˆํ•˜์—ฌ ์ž๋™์ฐจ ์‹œ์žฅ์—์„œ ๋ณ€ํ™”๋ฅผ ์ˆ˜๋ฐ˜ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋‚˜ํƒ€๋‚ด๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์šฐ๋ฆฌ๊ฐ€ ์‹œ์žฅ ๋ฐ์ดํ„ฐ์˜ ๊ฐ€์šฉ์„ฑ์„ ์ ์šฉํ•˜๋ ค๊ณ  ํ•  ๋•Œ, ์ž๋™์ฐจ ์‹œ์žฅ ๊ด€๋ จ ์ •๋ณด๋‚˜ ๊ฐ์„ฑ์˜ ์ž๊ธฐ์ƒ๊ด€์„ฑ์„ ์ž˜ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋ฉด, ๊ฐ์ • ๋ถ„์„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ํฐ ๊ธฐ์—ฌ๋ฅผ ํ•  ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์‹ค์ œ ์‹œ์žฅ์—์„œ์˜ ๋น„์ง€๋‹ˆ์Šค ์„ฑ๊ณผ์—๋„ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.List of Tables iv List of Figures v Abstract 1 1. Introduction 1.1 Back Ground 3 1.2 Necessity of Study 6 1.3 Purpose & Questions 8 1.4 Structure 9 2. Literature Reviews of VOC Analysis 2.1 Importance of VOC 11 2.2 Data Mining 15 2.2.1 Concept & Functionalities 15 2.2.2 Methodologies of Data mining 20 2.3 Text Mining 24 2.4 Sentiment Analysis 26 2.5 Research Trend in Korea 30 3. Methodology 3.1 Research Flow 32 3.2 Proposed Methodologies 34 3.2.1 Sentiment Analysis 34 3.2.2 Auto-correlation Analysis 37 3.2.3 Principal Component Analysis (PCA) 38 3.2.4 Linear Regression 40 4. Experiment & Analysis 4.1 Phase I: Constructing Sentiment Lexicon & 7 Sentiment Domains 43 4.1.1 The Subject of Analysis & Crawling Data 43 4.1.2 Extracting POS Information 44 4.1.3 Review Extracting POS Information 46 4.2 Phase II : Reliability Analysis 49 4.2.1 Auto-correlation Analysis of Sentiment 51 4.2.2 Principal Component Analysis of Sentiment 55 4.3 Phase III : Influence on Automotive Market Share 58 4.3.1 Linear Regression Model 58 4.3.2 Definition of Variables 60 4.3.3 The Result of Linear Regression Analysis 62 5. Conclusion 5.1 Summary of Study 73 5.2 Managerial Implication and Limitation 75 5.3 Future Study 77 References 79Docto

    TWEEZER โ€“ Tweets Analysis

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    Twitter is one in all the foremost used applications by the people to precise their opinion and show their sentiments towards different occasions. Sentiment analysis is an approach to retrieve the sentiment through the tweets of the general public. Twitter sentiment analysis is application for sentiment analysis of information which are extracted from the twitter(tweets). With the assistance of twitter people get opinion about several things round the nation .Twitter is one such online social networking website where people post their views regarding to trending topics .It s huge platform having over 317 million users registered from everywhere the globe. a decent sentimental analysis of information of this huge platform can result in achieve many new applications like โ€“ Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc. during this paper, we used two specific algorithm โ€“Naรฏve Bayes Classifier Algorithm for polarity Classification & Hashtag classification for top modeling. this system individually has some limitations for Sentiment analysis. The goal of this report is to relinquish an introduction to the present fascinating problem and to present a framework which is able to perform sentiment analysis on online mobile reviews by associating modified naรฏve bayes means algorithm with Naรฏve bayes classification

    Sentiment Analysis of Nigerian Studentsโ€™ Tweets on Education: A Data Mining Approach

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    The paper is aimed at investigating data mining technologies by acquiring tweets from Nigerian University students on Twitter on how they feel about the current state of the Nigerian university system. The study for this paper was conducted in a way that the tweet data collected using the Twitter Application was pre-processed before being translated from text to vector representation using a feature extraction technique such Bag-of-Words. In the paper, the proposed sentiment analysis architecture was designed using UML and the Naรฏve Bayes classifier (NBC) approach, which is a simple but effective classifier to determine the polarity of the education dataset, was applied to compute the probabilities of the classes. Furthermore, Naรฏve Bayes classifier polarized the tweets' wording as negative or positive for polarity. Based on our investigation, the experiment revealed after data cleaning that 4016 of the total data obtained were utilized. Also, Positive attitudes accounted for 40.56%, while negative sentiments accounted for 59.44% of the total data having divided the dataset into 70:30 training and testing ratio, with the Naรฏve Bayes classifier being taught on the training set and its performance being evaluated on the test set. Because the models were trained on unbalanced data, we employed more relevant evaluation metrics such as precision, recall, F1-score, and balanced accuracy for model evaluation. The classifier's prediction accuracy, misclassification error rate, recall, precision, and f1-score were 63 %, 37%, 63%, 62%, and 62% respectively. All of the analyses were completed using the Python programming language and the Natural Language Tool Kit packages. Finally, the outcome of this prediction is the highest likelihood class. These forecasts can be used by Nigerian Government to improve the educational system and assist students to receive a better education

    A Multimodal Approach to Sarcasm Detection on Social Media

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    In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly more difficult than categorizing sentiment. Because, in sarcasm, the intended meaning of the expression by the user is opposite to the literal meaning. Because of its complex nature, it is often difficult even for human to detect sarcasm without proper context. However, people on social media succeed in detecting sarcasm despite interacting with strangers across the world. That motivates us to investigate the human process of detecting sarcasm on social media where abundant context information is often unavailable and the group of users communicating with each other are rarely well-acquainted. We have conducted a qualitative study to examine the patterns of users conveying sarcasm on social media. Whereas most sarcasm detection systems deal in word-by-word basis to accomplish their goal, we focused on the holistic sentiment conveyed by the post. We argue that utilization of word-level information will limit the systems performance to the domain of the dataset used to train the system and might not perform well for non-English language. As an endeavor to make our system less dependent on text data, we proposed a multimodal approach for sarcasm detection. We showed the applicability of images and reaction emoticons as other sources of hints about the sentiment of the post. Our research showed the superior results from a multimodal approach when compared to a unimodal approach. Multimodal sarcasm detection systems, as the one presented in this research, with the inclusion of more modes or sources of data might lead to a better sarcasm detection model

    A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

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    In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content.  Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of  cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying  along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media

    Themes and Participantsโ€™ Role in Online Health Discussion: Evidence From Reddit

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    Health-related topics are discussed widely on different social networking sites. These discussions and their related aspects can reveal significant insights and patterns that are worth studying and understanding. In this dissertation, we explore the patterns of mandatory and voluntary vaccine online discussions including the topics discussed, the words correlated with each of them, and the sentiment expressed. Moreover, we explore the role opinion leaders play in the health discussion and their impact on participation in a particular discussion. Opinion leaders are determined, and their impact on discussion participation is differentiated based on their different characteristics such as their connections and locations in the social network, their content, and their sentiment. We apply social network analysis, topic modeling, sentiment analysis, machine learning, econometric analysis, and other techniques to analyze the collected data from Reddit. The results of our analyses show that sentiment is an important factor in health discussion, and it varies between different types of discussions. In addition, we identified the main topics discussed for each vaccine. Furthermore, the results of our study found that global opinion leaders have more influence compared to local opinion leaders in elevating the health discussion. Our study has important theoretical and practical implications

    IMPACT AND DIFFUSION OF SENTIMENT IN PUBLIC COMMUNICATION ON FACEBOOK

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    In recent years, political parties and politicians have begun to use public Facebook pages not only for the purpose of self-presentation but also to aim at entering into direct dialogues with citizens and enabling political discussions. Not only the owner of the page but also any people who are politically interested can create politically relevant postings on the Wall of the page. These Wall posts often exhibit sentiment associated with certain political topics, political parties or politicians. In this paper, we seek to examine whether sentiment occurring in Wall posts on public political Facebook pages has an effect on feedback in terms of the quantity of triggered comments. Based on a data set of 5,626 Wall posts from Facebook pages of German political parties and politicians, we find different significant relationships between the quantity of words indicating positive and negative emotions in a Wall post and the number of its corresponding comments. Furthermore, our results show that positive as well as negative emotions might diffuse in the subsequent comments

    MELex: a new lexicon for sentiment analysis in mining public opinion of Malaysia affordable housing projects

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    Sentiment analysis has the potential as an analytical tool to understand the preferences of the public. It has become one of the most active and progressively popular areas in information retrieval and text mining. However, in the Malaysia context, the sentiment analysis is still limited due to the lack of sentiment lexicon. Thus, the focus of this study is to a new lexicon and enhance the classification accuracy of sentiment analysis in mining public opinion for Malaysia affordable housing project. The new lexicon for sentiment analysis is constructed by using a bilingual and domain-specific sentiment lexicon approach. A detailed review of existing approaches has been conducted and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. The developed approach is able to analyze text for two most widely used languages in Malaysia, Malay and English, with better accuracy. The process of constructing MELex involves three activities: seed words selection, polarity assignment and synonym expansions, with four different experiments have been implemented. It is evaluated based on the experimentation and case study approaches where PR1MA and PPAM are selected as case projects. Based on the comparative results over 2,230 testing data, the study reveals that the classification using MELex outperforms the existing approaches with the accuracy achieved for PR1MA and PPAM projects are 90.02% and 89.17%, respectively. This indicates the capabilities of MELex in classifying public sentiment towards PRIMA and PPAM housing projects. The study has shown promising and better results in property domain as compared to the previous research. Hence, the lexicon-based approach implemented in this study can reflect the reliability of the sentiment lexicon in classifying public sentiments
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