Sentiment Classification Using Sentence-level Lexical Based Semantic Orientation of Online Reviews

Khan, Amin; Baharudin, Baharum; Khan, Khairullah

doi:10.3923/tasr.2011.1141.1157

Cited by 34 publications

(31 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current sentiment analysis methods disregard patterns of sentiments across sentences (Das and Chen 2007), instead they examine them at an aggregated message level (Tirunillai and Tellis 2012), or else derive it at a sentence level (Büschken and Allenby 2016;Khan, Baharudin, and Khan 2011). However, the active use of contradictory sentiment expressions (Fonic 2003) might relate to a lesser degree of conviction.…”

Section: Discourse Patternsmentioning

confidence: 99%

Unveiling What Is Written in the Stars: Analyzing Explicit, Implicit, and Discourse Patterns of Sentiment in Social Media

Ordenes

Ludwig

Ruyter

et al. 2017

Journal of Consumer Research

183

View full text Add to dashboard Cite

Deciphering consumers’ sentiment expressions from big data (e.g., online reviews) has become a managerial priority to monitor product and service evaluations. However, sentiment analysis, the process of automatically distilling sentiment from text, provides little insight regarding the language granularities beyond the use of positive and negative words. Drawing on speech act theory, this study provides a fine-grained analysis of the implicit and explicit language used by consumers to express sentiment in text. An empirical text-mining study using more than 45,000 consumer reviews demonstrates the differential impacts of activation levels (e.g., tentative language), implicit sentiment expressions (e.g., commissive language), and discourse patterns (e.g., incoherence) on overall consumer sentiment (i.e., star ratings). In two follow-up studies, we demonstrate that these speech act features also influence the readers’ behavior and are generalizable to other social media contexts, such as Twitter and Facebook. We contribute to research on consumer sentiment analysis by offering a more nuanced understanding of consumer sentiments and their implications.

show abstract

Section: Discourse Patternsmentioning

confidence: 99%

Unveiling What Is Written in the Stars: Analyzing Explicit, Implicit, and Discourse Patterns of Sentiment in Social Media

Ordenes

Ludwig

Ruyter

et al. 2017

Journal of Consumer Research

183

View full text Add to dashboard Cite

show abstract

“…This pattern annotated the manually rated the emoticons with different categorization. Emoticons are classified to improve the accuracy based on the sentence level lexicon based sentiment analysis [7,8].…”

Section: Literature Reviewmentioning

confidence: 99%

Lexicon based Acronyms and Emoticons Classification of Sentiment Analysis (SA) on Big Data

Edison¹,

Aloysius²

2017

IJDTA

View full text Add to dashboard Cite

show abstract

“…This mechanism mainly focus on splitting review document at above specified levels to determine expressed opinion whether it is positive ,negative or neutral. The task of summaries is clearly different from traditional text summarization [1] because it does not summarize the reviews by selecting or rewriting a subset of original sentences from the reviews.…”

Section: Introductionmentioning

confidence: 99%

Product Aspect Ranking Using Domain Dependent and Domain Independent Review

Sole¹

2014

IJRET

View full text Add to dashboard Cite

show abstract

Sentiment Classification Using Sentence-level Lexical Based Semantic Orientation of Online Reviews

Cited by 34 publications

References 9 publications

Unveiling What Is Written in the Stars: Analyzing Explicit, Implicit, and Discourse Patterns of Sentiment in Social Media

Unveiling What Is Written in the Stars: Analyzing Explicit, Implicit, and Discourse Patterns of Sentiment in Social Media

Lexicon based Acronyms and Emoticons Classification of Sentiment Analysis (SA) on Big Data

Product Aspect Ranking Using Domain Dependent and Domain Independent Review

Contact Info

Product

Resources

About