An approach towards comprehensive sentimental data analysis and opinion mining

Kherwa, Pooja; Sachdeva, Arjit; Mahajan, Dhruv; Pande, Nishtha; Singh, Prashast Kumar

doi:10.1109/iadcc.2014.6779394

Cited by 21 publications

(10 citation statements)

References 6 publications

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“…After testing, it could presented that the proposed method can provide more effectiveness for sentiment classification based on content of text. In [11], saying that the world wide web can be viewed as a repository of opinions from users spread across different networks and websites, and today's citizens look up reviews and opinions for judging commodities, visit forums to debate about policies and events. With this bombardment in the faith and volume of user reviews and opinions, manufacturers and retailers face the difficult task of automating the analysis of big amount of data (reviews of users, opinions, sentiments).…”

Section: Literature Surveymentioning

confidence: 99%

Review analyzer analysis of product reviews on WEKA classifiers

Kshirsagar

Deshkar

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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E-transactions have become promising and very much convenient due to worldwide and usage of the internet. The consumer reviews are increasing rapidly in number on various products. These large numbers of reviews are beneficial to manufacturers and consumers alike. It is a big task for a potential consumer to read all reviews to make a good decision of purchasing. It is beneficial to mine available consumer reviews for popular products from various product review sites of consumer. The first step is performing sentiment analysis to decide the polarity of a review. On the basis of polarity, we can then classify the review. Comparison is made among the different WEKA classifiers in the form of charts and graphs. (Abstract) Index Terms-Online Reviews, Polarity of Reviews, WEKA Classifier. (key words)

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Section: Literature Surveymentioning

confidence: 99%

Review analyzer analysis of product reviews on WEKA classifiers

Kshirsagar

Deshkar

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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“…Dependencies in a sentence shows the relations between words in a sentence. After this, only the relevant set of dependencies are kept and others are ignored as in [1].…”

Section: Sentiment Analysis Using Sentiwordnetmentioning

confidence: 99%

“…Step-5:After identifying the features, we need to do sentiment classification using SentiWordNet. The method used for sentiment classification is similar to the one used in [1].…”

Section: Sentiment Analysis Using Sentiwordnetmentioning

confidence: 99%

“…RELATED WORK Summarizing the results of aspect level sentiment analysis using data visualisation API is necessary in order to give the user a detailed result for analysing the product. The work of Kherwa Pooja et al [1] presents such an approach. Aspects of the product are identified manually and SentiWordNet [12] is used for sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

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Extended Comprehensive Sentiment Analysis for Informal Opinion Text

Kataria¹,

Shah²

2015

IJERT

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There has been a remarkable increase in use of E-commerce websites, the World Wide Web can now be seen as a repository of reviews and opinions from users spread across various websites and networks. Comprehensive feedback about the product can help the company know what the users liked and disliked about their product. People tend to adapt modern writing styles like misspelled words, abbreviations, concatenated words and emoticons, considering these can increase the accuracy of sentiment analysis. Moreover people tend to use different words for a particular feature of the product. Thus, identifying frequent nouns and noun phrases automatically will help classify more number of reviews and also help in identifying any new feature of the product that is being talked about.

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“…The subjectivity strongly depends on its sentences or messages. The novelty of lexicon and its semantics can be used to distinguish between words and phrases in determining the polarity of the sentiment [ 20 , 21 ]. The modified item response theory helps to achieve personalized learning and provide learning pathways and helps them to learn effectively [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Topical Sentiment Analysis for the Classification of E‐Learners and Their Topics of Interest

Ravichandran

Kulanthaivel

Chellatamilan

2015

The Scientific World Journal

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Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervised algorithm named bigram item response theory (BIRT). It differs from the traditional classification and document level classification algorithm. The investigation illustrated in this paper is of threefold which are listed as follows: (1) lexicon based sentiment polarity of tweet messages; (2) the bigram cooccurrence relationship using naïve Bayesian; (3) the bigram item response theory (BIRT) on various topics. It has been proposed that a model using item response theory is constructed for topical classification inference. The performance has been improved remarkably using this bigram item response theory when compared with other supervised algorithms. The experiment has been conducted on a real life dataset containing different set of tweets and topics.

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An approach towards comprehensive sentimental data analysis and opinion mining

Cited by 21 publications

References 6 publications

Review analyzer analysis of product reviews on WEKA classifiers

Review analyzer analysis of product reviews on WEKA classifiers

Extended Comprehensive Sentiment Analysis for Informal Opinion Text

Intelligent Topical Sentiment Analysis for the Classification of E‐Learners and Their Topics of Interest

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