With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.

A survey on opinion mining and sentiment analysis

Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user's sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.

Aspect extraction in sentiment analysis: comparative analysis and survey

Predicting hotel review helpfulness

Social media has become a vital part of social life. It affects the beliefs, values, and attitudes of people, as well as their intentions and behaviors. Meanwhile, social media enables governments and organizations to engage people while allowing consumers to make informed decisions. Therefore, converting social media content into information, key concepts, and themes is crucial for generating knowledge and formulating strategies. In this paper, we introduce a grounded theory approach that involves (i) defining the goal and scope of a study; (ii) logically and systematically identifying social media sources, total sample size, and the sample size of every source category; (iii) employing computer-aided lexical analysis with statistical and graphical methods to identify the key dimensions of the topic while minimizing human errors, as well as coding and categorization biases; and (iv) interpreting the findings of the study. This systematic approach was illustrated with the destination image of Macao as an example. The proposed methodology with its hybrid nature can quantitatively analyze qualitative social media content (e.g., impressions, opinions, and feelings) and can identify emergent concepts from the ground up. This paper contributes to electronic commerce research by presenting a novel approach for extracting, analyzing, and understanding social media content.Keywords: Social media; Content analysis; Lexical and statistical approaches; Concept formation(ProQuest: ... denotes formulae omitted.)1. IntroductionThe Internet has attracted the attention of research communities [Comley, 2008; Dwivedi et al., 2008; Zwass, 1996]. In particular, the significant role of analyzing social media and networks to advance our understanding of information sharing, communication [Averya et al., 2010; Chiu et al., 2006; Turri et al., 2013], opinion formation, and dissemination has been recognized [Abrahams et al., 2012; Airoldi et al., 2006; Bai, 2011; Jansen et al., 2011; Lane et al., 2012]. Nevertheless, rigorous, quantitative studies on social media content, particularly on electronic commerce and information management, remain scarce [Bai, 2011]. The most considerable barrier to social media usage is the lack of a versatile methodology for selecting, collecting, processing, and analyzing conte xtual information obtained from social media sites. However, several software companies have developed proprietary text mining systems for data visualization [Arnold, 2012], and researchers have developed expert systems for sentiment analysis [Abrahams et al., 2012; Lane et al., 2012].Nevertheless, social media content is widely accessible, up-to-date, and available in electronic format. Therefore, a systematic approach is necessary, as it helps electronic commerce researchers, organizations, and governments understand the commonality in various online text data that appear in social media. Using the information obtained from social media, researchers can gain valuable insights into the beliefs, values, attitudes, and perceptions of social media users with regard to the utility of user-generated content and trust formation [Karimov et al., 2011; Kim et al., 2012; Wang & Li, 2014]. Consequently, such information can help marketers monitor the perceptions of people regarding social networks and aid organizations in strategic planning.To address the gap between the availability of user-generated raw text and the contextual information of aggregated data, the present study introduces a grounded theory approach [Strauss & Corbin, 1988] to analyze social media content to identify the underlying factor structure of the collected information and to interpret the identified structure in relation to the study objective. Strauss and Corbin [1998] defined the grounded theory approach as a research method that employs a systematic set of procedures to develop an "inductively derived" grounded theory about a particular phenomenon. …

http://web.csulb.edu/journals/jecr/issues/20152/Paper5.pdf

Content Analysis of Social Media: A Grounded Theory Approach

Businesses are currently using social media analytics SMA to develop insights for improving performance and productivity across different functions. The SMA knowledge is growing diversely, and there is a need to understand the trends and approaches holistically. The present paper offers a comprehensive review of the SMA empirical literature and directions for future research. The review is based on 54 papers selected out of 843 search results. The review focuses on different domains: industrial domains, data-mining objectives, use cases, and applications. Out of the studies, public administration and consumer discretionary sectors are the dominant ones with Twitter data being used in most of the analysis. Out of the possible techniques, classification techniques and regression models are more popular than others. Stakeholder engagement is the most focused theme in the research studies. The review also offers insights into which analytical approaches are being used in which industrial domains for specific decision making. It further suggests that novel methods, such as cross-media data classification, tags detection, label priority ranking, tweeting activity signatures, and geospatial data processing have been used less and could be further explored in future research. The review also offers implications for the decision sciences domain.

Social Media Analytics: Literature Review and Directions for Future Research

EXPRS: An extended pagerank method for product feature extraction from online consumer reviews

Consumers are increasingly relying on other consumers' online reviews of features and quality of products while making their purchase decisions. However, the rapid growth of online consumer product reviews makes browsing a large number of reviews and identifying information of interest time consuming and cognitively demanding. Although there has been extensive research on text review mining to address this information overload problem in the past decade, the majority of existing research mainly focuses on the quality of reviews and the impact of reviews on sales and marketing. Relatively little emphasis has been placed on mining reviews to meet personal needs of individual consumers. As an essential first step toward achieving this goal, this study proposes a product feature-oriented approach to the analysis of online consumer product reviews in order to support feature-based inquiries and summaries of consumer reviews. The proposed method combines LDA (Latent Dirichlet Allocation) and a synonym lexicon to extract product features from online consumer product reviews. Our empirical evaluation using consumer reviews of four products shows higher effectiveness of the proposed method for feature extraction in comparison to association rule mining.Keywords: Online product reviews; Feature extraction; Latent Dirichlet Allocation; Synonym lexicon; Data mining(ProQuest: ... denotes formulae omitted.)1. IntroductionWith the rapid advance of Internet technology, online shopping becomes increasingly popular. iResearch reports that the e-commerce market worldwide reached US$1.99 billion in Fall 2012, which was 21.9% more than 2011 [iResearch 2012], However, online shopping makes it impossible for consumers to obtain the first-hand experience and knowledge about product quality through direct touching, seeing, and trying out products before purchase.With the rise of Web 2.0 technology, users have become more and more comfortable with sharing their opinions on products or services over the Internet. Examples of these Web 2.0 web sites include e-commerce sites such as Amazon.com, social network sites like Facebook.com, local service provider review sites like Yelp.com, and digital media distributors like Netflix.com. Empirical studies have shown that user reviews indeed have significant impact on sales, and users rely much more on reviews than on numeric ratings to decide if a product should be purchased [Chevalier & Mayzlin, 2006, Li et al., 2009], Consumers often rely on other consumers' online reviews of features and quality of products while making purchase decisions because those reviews offer more useful information about products than the descriptive information available on a shopping website provided by manufacturers [Park and Kim 2008, Hu et al. 2009], According to a survey, 70 percent of Americans regularly consult online consumer product reviews or ratings before making an important purchase [Ante 2009],The rapid growth of the volume of online consumer product reviews, however, makes browsing a large number of reviews to identify information of interest time consuming and cognitively demanding. It poses a significant challenge for taking advantage of those reviews. Review mining that uses data mining and text mining techniques for analyzing a large quantity of online consumer product reviews [Popescu and Etzioni 2007] has been extensively explored as a means of addressing this information overload problem. The majority of existing research, however, focuses on prediction of helpfulness of reviews, determining the polarity of consumer opinions on products, and the impact of reviews on sales and marketing, etc. Relatively little emphasis has been placed on mining reviews to understand consumers' opinions toward individual features of specific products. Product features include product attributes, components, component attributes, etc. [Xi et al. 2011],In reality, individual consumers may have different preferences for features of a product. …

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An Lda and Synonym Lexicon Based Approach to Product Feature Extraction from Online Consumer Product Reviews

Consumers often view online consumer product review as a main channel for obtaining product quality information. Existing studies on product review sentiment analysis usually focus on identifying sentiments of individual reviews as a whole, which may not be effective and helpful for consumers when purchase decisions depend on specific features of products. This study proposes a new feature-level sentiment analysis approach for online product reviews. The proposed method uses an extended PageRank algorithm to extract product features and construct expandable context-dependent sentiment lexicons. Moreover, consumers’ sentiment inclinations toward product features expressed in each review can be derived based on term dependency relationships. The empirical evaluation using consumer reviews of two different products shows a higher level of effectiveness of the proposed method for sentiment

Baizhang Ma

Papers

EXPRS: An extended pagerank method for product feature extraction from online consumer reviews

An Lda and Synonym Lexicon Based Approach to Product Feature Extraction from Online Consumer Product Reviews

A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships Submission Type: Completed Research Paper

A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships.