ANALYSIS ON TWITTER DATA OF AUTOMOBILE DOMAIN USING ONTOLOGY

Priya Gupta

Abstract


Corpus Data refers to as a collection of huge datasets. Sentiment analysis contributed to a popular research area for twitter. The sentiment analysis done without feature extraction fails to give the deep result about the users opinion but, features of the domain are extracted by building ontology which helps in getting the refined sentiment analysis. Ontology means a formal, explicit specification of a shared conceptualization. Conceptualization refers to an abstract model of some world phenomena. In this paper, we have used ontology to analyse the tweets to increase augmentation and efficiency of sentiments which is obtained using naïve Bayesian algorithm. The work is done in the following stages. In first stage, the tweets are extracted from Twitter4J and stored in a repository. Then sentences are extracted one by one. Sentences extracted are simplified by removing stop words and redundant words. In Second stage, the words left in the sentences are used for sense matching using WordNet-an online semantic dictionary. WordNet dictionary is used to extract features from tweets. In Third stage, Ontology is being generated by using java customized code. Crawler is being designed next to get the details about the automobile domain. The data is stored in text manner. In fourth stage, Mapping of data is done which includes mapping of ontology with the crawler data, together with ontology validation. In fifth stage, Analysis of tweets is done using ontology by applying naïve Bayesian algorithm and comparison of automobile is done which one is better and what all are the attributes that other automobile does not fall into this category.

Keywords


Sentiment analysis, Ontology, Tweets, Twitter4J

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References


. Efstratios Kontopoulos and Christos Berberidis, “Expert Systems with Applications”, www.elsevier.com/locate/eswa, 4065–4074, 2015.

. K. Vithiya Ruba and D. Venkatesan, “Building a Custom Sentiment Analysis Tool based on an Ontology for Twitter Posts”, Indian Journal of Science and Technology, Vol 8(13), July 2015.

. Ms. Swaminarayan Priya R. et al, “A Comprehensive study of Query Languages for Semantic Web and retrieval of data from University Ontology Using SPARQL” , International Journal of Information and Computing Technology” ISSN: 0976 – 5999.

. Ms. Swaminarayan Priya R. et al, “Knowledge Representation of Published Articles in Semantic Web using Upper Ontology”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 8, August 2012.

. R. Baracho, G. Silva, and L. Ferreira, “Sentiment Analysis in Social Networks: a Study on Vehicles” ONTOBRAS-MOST, CEUR Workshop Proceedings, page 132-143, volume 938. 2012

. Gopinath Ganapathy and S. Sagayaraj, “Automatic Ontology Creation by Extracting Metadata from the Source code”, Global Journal of Computer Science and Technology, Vol.10, Issue 14, Ver.1.0, November 2010.


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