A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

Ravi, Kumar; Ravi, Vadlamani

doi:10.1016/j.knosys.2015.06.015

Cited by 1,097 publications

(600 citation statements)

References 195 publications

(409 reference statements)

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“…To bridge the gap, there have been attempts to translate some open source English resources into other languages by using the machine translation service. But every language has its peculiarities, which may render the adapting technique unsuitable, so one of the challenges of SA is to create specific linguistic resources for different languages (Balahur et al 2014;Montoyo et al 2012;Ravi & Ravi 2015;Taboada 2016;Zhang et al 2014).…”

Section: Identifying Polarity In Different Text Typesmentioning

confidence: 99%

“…movie reviews, hotel reviews), forums, blogs, micro-blogs ("tweets"), news articles and social media in order to disclose the writer's opinions, attitudes and emotions toward individuals, events or topics (Pang & Lee 2008;Ravi & Ravi 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Accuracy has been increased by including all parts of speech, instead of being confined to adjectives, as well as by considering the position of the word in the text. SVM has generally given better results than Naïve Bayes, but Naïve Bayes also performs excellently, in particular if the feature space is small (Pang et al 2002;Ravi & Ravi 2015;Taboada 2016;Vegda et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Identifying Polarity in Different Text Types

Pajupuu¹,

Altrov²,

Pajupuu³

2016

FEJF

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While Sentiment Analysis aims to identify the writer's attitude toward individuals, events or topics, our aim is to predict the possible effect of a written text on the reader. For this purpose, we created an automatic identifier of the polarity of Estonian texts, which is independent of domain and of text type. Depending on the approach chosen -lexicon-based or machine learning -the identifier uses either a lexicon of words with a positive or negative connotation, or a text corpus where orthographic paragraphs have been annotated as positive, negative, neutral or mixed. Both approaches worked well, resulting in a nearly 75% accuracy on average. It was found that in some cases the results depend on the text type, notably, with sports texts the lexicon-based approach yielded a maximum accuracy of 80.3%, while over 88% was gained for opinion stories approached by machine learning.

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Section: Identifying Polarity In Different Text Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Polarity in Different Text Types

Pajupuu¹,

Altrov²,

Pajupuu³

2016

FEJF

View full text Add to dashboard Cite

show abstract

Section: Identifying Polarity In Different Text Typesmentioning

confidence: 99%

“…SA has mostly been performed for product reviews (incl. movie reviews, hotel reviews), forums, blogs, micro-blogs ("tweets"), news articles and social media in order to disclose the writer's opinions, attitudes and emotions toward individuals, events or topics (Pang & Lee 2008;Ravi & Ravi 2015).…”

Section: Introductionmentioning

confidence: 99%