An empirical study of sentence features for subjectivity and polarity classification

Chenlo, José M.; Losada, David E.

doi:10.1016/j.ins.2014.05.009

Cited by 76 publications

(23 citation statements)

References 37 publications

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“…), from various combinations of features, such as: word n-grams [9], [10], [11], POS tags [12], syntax features and syntactic dependency (e.g., hashtags, punctuations, parsing etc.) that can determine the meaning of a sentence [13], [28], [29], [49], with/without words' prior sentiment and semantic concepts [14].…”

Section: Supervised Machine Learning Approachesmentioning

confidence: 99%

Rich Semantic Sentiment Analysis Using Lexicon Based Approach

Lodin

Balani

2017

IJSC

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Web is a huge repository of information, and a massive amount of data is generated everyday on online platforms. Information, can be facts and opinions, facts are objective statements about an event, and opinions are subjective statements that reflect the sentiments of a person towards an event. Research on sentiment analysis has increased tremendously in recent years due to its wide variety of applications. To analyze sentiments, certain methods have been proposed, which can be broadly categorized as supervised machine learning and lexicon based approaches. Supervised machine learning methods are giving high accuracy but these methods need training data and are domain dependent, while lexicon-based methods are not domain dependent. Although, building of lexicon is costly, but once constructed, it can be applied for a wide variety of domains, but still lexicon based methods are restricted to their dictionaries and are full-dependent on the presence of terms that explicitly reflect the sentiment, while in many cases the sentiment of a term is implicitly reflected by the semantics of its context. Therefore, we've proposed context aware, semantically rich (conceptual & contextual semantics) lexicon-based method which is different from traditional lexicon-based methods that assigns sentiment score and strength to terms in a dynamic way, and outperforms baselines.

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Section: Supervised Machine Learning Approachesmentioning

confidence: 99%

Rich Semantic Sentiment Analysis Using Lexicon Based Approach

Lodin

Balani

2017

IJSC

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“…Recently, deep learning has been used to resolve this problem [45]. The other stream of research on tweets is predicting tweets' subjectivity and objectivity [8]. The spammer detection is the endemic problem of Twitter.…”

Section: Supervised Approach In Twittermentioning

confidence: 99%

Spoiler detection in TV program tweets

Jeon

Kim

2016

Information Sciences

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“…But only few related works are presented in this paper as follows. Jose M. Chenlo et al [10] demonstrated wide range of features such as n-grams, Part-of-speech, Location based features, Lexicon based features, Syntactic features, Structural or discourse features, and etc., for sentiment classification. In [9], the OMSA approach is presented with different frameworks and algorithms as a review and their results were compared and analyzed for readily available datasets.…”

Section: Related Workmentioning

confidence: 99%

“…An entity is the hierarchical representation of components and subcomponents. Each component is associated with set of attributes, whereas the large amount of documents is processed for sentiment with different features such as n-grams, part-of-speech, location based features, lexicon based features, syntactic features, structural or discourse features, and etc., [10].…”

Section: Introductionmentioning

confidence: 99%