A RESEARCH PAPER ON PRODUCT REVIEW BASED ON GEOGRAPHIC LOCATION USING SVM APPROACH IN TWITTER

Ms. Sujal Shah , Ms. Khushali Mistry

Abstract


Many organizations do distinctive sorts of overviews like Product quality study, aggressive items and market study, mark audit study, client benefit review, new item acknowledgment and request study, client trust and steadfastness study and numerous different studies for the organization and item upgrades. These sort of reviews need parcel of spending plan, labor and part of time. The report produced by this procedure won't not be certified. This is tedious, high spending plan included and manual process. Online informal organization (OSNs, for example, Facebook, Google+, and Twitter has changed the present framework in many measurements. Twitter will useful for company to grow their business ideas and launching new products.


Keywords


Sentiment Analysis, Social Media, Twitter, Machine Learning Methods: Support Vector Machine, K-Neatest Neighbour, Naïve Bayes, pre-processing, Feature Extraction, Opinion Mining Unigram, Bigram, Trigram, N-gram

Full Text:

PDF

References


Jao Allen Banados, Kurt Junshean Espinosa “Optimizing Support Vector Machine in Classifying Sentiments on Product Brands from Twitter” IEEE 2014.

Zhao jianqiang “Pre-processing Boosting Twitter Sentiment Analysis” China IEEE 2015.

Ashish Shukla, Rahul Misra “ Sentiment Classification and Analysis Using Modified K-Means and Naive Bayes Algorithm IJAR 2015.

Diego Terrana, Agnese Augello, Giovanni Pilato “Automatic Unsupervised Polarity Detection on a Twitter Data Stream” IEEE 2014.

Rajni Singh, Rajdeep Kaur “ Sentiment Analysis on Social Media and Online Review” IJCA 2015.

Walaa Medhat , Ahmed Hassan , Hoda Korashy “Sentiment analysis algorithms and applications: A Survey” Elsevier 2014.

Bogdan Batrinca, Philip C, Treleaven “Social media analytics: a survey of techniques, tools and platforms” Springer 2015.

Alexander Pak, Patrick Paroubek “Twitter as a Corpus for Sentiment Analysis and Opinion Mining” 2015.

Mrs.R.Nithya, Dr.D.Maheswari “Sentiment Analysis On Unstructured Review” IEEE 2014.

Suchita V Wawre, Sachin N Deshmukh “Sentiment Classification using Machine Learning Techniques” IJSR 2016.

Xing Fang, Justin Zhan “Sentiment analysis using product review data” Springer 2015.

Mohsen Farhadloo, Erik Rolland “Multi-Class Sentiment Analysis with Clustering and Score Representation” IEEE 2013.

Moon Gie Kim, June Hwan Koh “Recent research trends for geospatial information explored

by Twitter data” Springer 2016.

Ana Mihanovic, Hrvoje Gabelica, zivko Krstic “Big Data and Sentiment Analysis using KNIME:

Online Reviews vs. Social Media” MIPRO 2014.


Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2019 INTERNATIONAL EDUCATION AND RESEARCH JOURNAL