Opinion mining about a product by analyzing public tweets in Twitter

Das, Tapan Kumar; Acharjya, D. P.; Patra, Manas Ranjan

doi:10.1109/iccci.2014.6921727

Cited by 55 publications

(21 citation statements)

References 5 publications

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“…They use good quality training set for better performance and results. Das et al (2014) developed an application that collected data from twitter, analyzed it with and generate reports containing tables and pie chart graphs. Shrivatava et al (2014) introduced an efficient method to classify the features of tweets and uses support vector machine to classify the tweets and attain an accuracy of 70.5 %.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical comparison of sentiment analysis techniques for social media

Hameed

2018

Int. j. adv. appl. sci.

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Nowadays the excessive use of internet produces a huge amount of data due to the social networks such as Twitter, Facebook, Orkut and Tumbler. These are microblogging sites and are used to share the people opinions and suggestions on daily basis relevant to the certain topic. These are beneficial for decision making or extracting conclusions. Analysis of these feeds aims to assess the thinking and comments of people about some personality or topic. Sentiment analysis is a type of text classification and is performed by various techniques such as Machine Learning Techniques and shows that the text is negative, positive or neutral. In this work, we provide a comparison of most recent sentiment analysis techniques such as Naïve Bayes, Bagging, Random Forest, Decision Tree, Support Vector Machine and Maximum entropy. The purpose of the study is to provide an empirical analysis of existing classification techniques for social media for analyzing the good performance and better information retrieval. A comprehensive comparative framework is designed to compare these techniques. Various benchmark datasets (UCI, KEEL) available in different repositories are used for comparison purpose. We presented an empirical analysis of six classifiers. The analysis results that Support Vector Machine performs much better as compared to other. Efforts are made to provide a conclusion about different algorithms on the basis of numerical and graphical metrics to conclude that which algorithm is optimal.

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Section: Literature Reviewmentioning

confidence: 99%

Empirical comparison of sentiment analysis techniques for social media

Hameed

2018

Int. j. adv. appl. sci.

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“…A framework to analyze data was discussed by Das and Kumar [12]. Similarly detail explanation of data analysis for public tweets was also discussed by Das et al in their paper [13].…”

Section: A Data Storage and Analysismentioning

confidence: 99%

A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools

Acharjya¹,

P²

2016

ijacsa

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Abstract-A huge repository of terabytes of data is generated each day from modern information systems and digital technologies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Additionally, it opens a new horizon for researchers to develop the solution, based on the challenges and open research issues.

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“…There are various approaches for mining twitter data. A system that processes the tweets by pulling data from tweeter posts was developed by Das et al [1]. Data collected from twitter were preprocessed and connected to Alchemy API.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Tweets for Inferring Popularity of Mobile Phones

Krishnan¹,

Elayidom²,

Santhanakrishnan³

2017

IJCA

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Sentiment analysis is a very popular technique for social network analysis. Sentiment analysis also termed as opinion mining is a process of automatically extracting knowledge from sentiments or opinions of others about some topic or problem. We can identify opinions in a large unstructured/structured data and analyze the polarity of opinions. Twitter is a large and rapidly growing micro blogging social networking website where people express their opinions in a short and simple manner of expressions. It is a common practice that merchants selling products on the Web ask their customers to review the products. In twitter number of customer reviews on different products is appearing. Mobile phones are a common domain in which number of customer reviews appears. This makes it difficult for a potential customer to read them in order to make a decision on whether to buy the product. We are only interested in the specific features of the phones that customers have opinions on and also whether the opinions are positive or negative. This paper presents a lexicon based approach for analyzing the customer reviews on mobile phones over Twitter data to measure the popularity based on which the customer can decide whether to buy the product.

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Opinion mining about a product by analyzing public tweets in Twitter

Cited by 55 publications

References 5 publications

Empirical comparison of sentiment analysis techniques for social media

Empirical comparison of sentiment analysis techniques for social media

A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools

Sentiment Analysis of Tweets for Inferring Popularity of Mobile Phones

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