Decision Making Using Sentiment Analysis from Twitter

M.Vasuki,; Arthi, J.; K.Kayalvizhi,

doi:10.15680/ijircce.2014.0212006

Cited by 4 publications

(1 citation statement)

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“…The internet age has altered the way people express their feelings and thoughts. People are linking each other with the support of internet through online conservation forums, blog post and much more that can be used for decision-making (Amolik et al, 2015;Neethu and Rajasree, 2013;Vasuki et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Grammar rule-based sentiment categorisation model for classification of Tamil tweets

Ravishankar

Shriram

2018

IJISTA

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The advent of social media has enabled people to easily and publicly express their ideas on a movie/product in such a way that it reaches millions of people within no time. This research aims to implement a tool that would be helpful in predicting the genre of the movies as perceived by the audience through linguistic rules and natural language processing (NLP) tool kit. This paper focuses on development of rule-based sentiment categorising tool for Tamil tweets and a tool has been developed using Python and NLP tool kit. Furthermore, a model is designed to determine the opinion along with genre classification of Tamil movies. For this work, a set of genres are selected from Tamil movies with public tweets based on sentiment analysis. We find that the tool classifies the genre of a particular movie provided by user tweets and validated our approach with domain experts and baseline models.

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Section: Introductionmentioning

confidence: 99%

Grammar rule-based sentiment categorisation model for classification of Tamil tweets

Ravishankar

Shriram

2018

IJISTA

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Detection of Early Depression Signals Using Social Media Sentiment Analysis on Big Data

Nair

Ashok

Pai

et al. 2021

Computer Networks and Inventive Communication Technologies

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Sentiment Analysis of Product Reviews in the Absence of Labelled Data Using Supervised Learning Approaches

Waqar

Mushtaq

Junejo³

et al. 2020

MJCS

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With the growing pace of internet usage, there is a vast variety of diverse individual opinions and thoughts avail-able online. Consumer reviews can act as a feedback and as well as a pool of ideas for which they can be of immense importance to any business. With the growth and popularity of opinion-rich resources such as online review sites and personal blogs, people now can and do, actively use information technology to seek out and un-derstand the opinions of others to decide whether to buy a product or not. Social media websites such as Facebook, Twitter, and e-commerce websites such as eBay, Amazon, etc. are being widely used to communicate viewpoints effectively. Assigning a positive or a negative sentiment to these reviews can help companies understand their users and also help users to make better decisions. Sentiment analysis being a challenging task can be tackled using supervised machine learning techniques or through unsupervised lexicon based approaches if labelled data is unavailable. In this study, we show that in absence of labelled product reviews of a particular website, labelled product reviews from a different website can be effectively used to train the supervised techniques to achieve a comparable performance to the unsupervised lexicon based approaches. This approach also benefits by covering all of the product reviews which the lexicon based approaches fail to do so. We deduce this by comparing five su-pervised approached and three lexicon based approaches on iPhone 5s reviews gathered from Amazon, Facebook, and Reevoo blog. Furthermore, we also found that unigram features combined with bigram features give the best results, and the effect of varying the training data size on the performance of ML classifiers in some cases was significant whereas in other cases it did not have any effect. Our results also suggest that reviews from Amazon are easiest to classify, followed by reviews from Reevo, and Facebook reviews are the hardest to classify.

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Decision Making Using Sentiment Analysis from Twitter

Cited by 4 publications

References 0 publications

Grammar rule-based sentiment categorisation model for classification of Tamil tweets

Grammar rule-based sentiment categorisation model for classification of Tamil tweets

Detection of Early Depression Signals Using Social Media Sentiment Analysis on Big Data

Sentiment Analysis of Product Reviews in the Absence of Labelled Data Using Supervised Learning Approaches

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