A Survey on Sentiment Analysis and Opinion Mining Techniques

Kaur, Amandeep; Gupta, Vishal

doi:10.4304/jetwi.5.4.367-371

Cited by 75 publications

(42 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the Table 7 below, we compare our model's results with many researches related to the decision tree for sentiment classification in (Mita 2011;Taboada et al 2008;Nizamani et al 2012;Wan et al 2015;Winkler et al 2015;Vinodhini and Chandrasekaran 2013, 2007, 2014Kaur et al 2015;Prasad et al 2016Prasad et al , 2014Sharma 2014).…”

Section: Resultsmentioning

confidence: 99%

“…There are many researches related to a decision tree for sentiment classification in (Mita 2011;Taboada et al 2008;Nizamani et al 2012;Wan et al 2015;Winkler et al 2015;Psomakelis et al 2015;Vinodhini and Chandrasekaran 2013, 23;Mandal et al 2014;Kaur et al 2015;Prasad et al 2016;Pong-Inwong et al 2014;Mugdha;Sharma 2014;Park et al 2003;Loh and Mauricio 2003). Automatic Text Classification (Mita 2011) is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A C4.5 algorithm for english emotional classification

Ngoc

et al. 2017

Evolving Systems

View full text Add to dashboard Cite

negative polarity are created by the decision tree. Classifying sentiments of one English document is identified based on the association rules of the positive polarity and the negative polarity. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. We have tested our new model on our testing data set and we have achieved 60.3% accuracy of sentiment classification on this English testing data set.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A C4.5 algorithm for english emotional classification

Ngoc

et al. 2017

Evolving Systems

View full text Add to dashboard Cite

show abstract

“…TECHNIQUES/APPROACHES FOR SENTIMENT ANALYSIS Two techniques are commonly used for sentiment analysis which is supervised learning technique [15] and unsupervised learning approach [30].…”

Section: IIImentioning

confidence: 99%

Sentiment Analysis: A Survey

Rani¹

2017

IJRASET

View full text Add to dashboard Cite

Sentiment analysis or opinion mining classifies the human's opinion or reviews into the positive, negative and neutral class which are written in form of text about some topic. Social networking sites such as Twitter, Facebook etc are rich in comments, customer reviews, opinion and sentiments. This paper describes the study of different sentiment analysis methods on different web resources such as review sites, blogs, discussion forums and news. It also gives overview of tools and application area of sentiment analysis.

show abstract

“…(Kaur and Gupta, 2013) surveyed sentiment analysis for different Indian languages including Telugu, but never mentioned about the corpus used. (Mukku et al, 2016) did sentiment classification for Telugu Figure 1: Process of building the resource text using various ML techniques, but no data was publicly made available.…”

Section: Related Workmentioning

confidence: 99%

ACTSA: Annotated Corpus for Telugu Sentiment Analysis

Mukku¹,

Mamidi²

2017

Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems

View full text Add to dashboard Cite

Sentiment analysis deals with the task of determining the polarity of a document or sentence and has received a lot of attention in recent years for the English language. With the rapid growth of social media these days, a lot of data is available in regional languages besides English. Telugu is one such regional language with abundant data available in social media, but it's hard to find a labelled data of sentences for Telugu Sentiment Analysis. In this paper, we describe an effort to build a gold-standard annotated corpus of Telugu sentences to support Telugu Sentiment Analysis. The corpus, named ACTSA (Annotated Corpus for Telugu Sentiment Analysis) has a collection of Telugu sentences taken from different sources which were then preprocessed and manually annotated by native Telugu speakers using our annotation guidelines. In total, we have annotated 5410 sentences, which makes our corpus the largest resource currently available. The corpus and annotation guidelines are made publicly available.

show abstract

A Survey on Sentiment Analysis and Opinion Mining Techniques

Cited by 75 publications

References 11 publications

A C4.5 algorithm for english emotional classification

A C4.5 algorithm for english emotional classification

Sentiment Analysis: A Survey

ACTSA: Annotated Corpus for Telugu Sentiment Analysis

Contact Info

Product

Resources

About