Real Time Text Analysis
K. Senthilkumar,E. Ruchika Mehra Vijayan +1 more
- Vol. 263, Iss: 4, pp 042005
TLDR
This paper aims to illustrate real time analysis of large scale data using distributed computation by performing sentiment analysis on live Twitter feeds for each individual tweet using sentiWordNet, a polarity assigned wordNet sample by Princeton University.Abstract:
This paper aims to illustrate real time analysis of large scale data. For practical implementation we are performing sentiment analysis on live Twitter feeds for each individual tweet. To analyze sentiments we will train our data model on sentiWordNet, a polarity assigned wordNet sample by Princeton University. Our main objective will be to efficiency analyze large scale data on the fly using distributed computation. Apache Spark and Apache Hadoop eco system is used as distributed computation platform with Java as development languageread more
References
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Proceedings Article
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.
TL;DR: This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.
Journal ArticleDOI
Contextual semantics for sentiment analysis of Twitter
TL;DR: Different from typical lexicon-based approaches, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly.
Journal ArticleDOI
Sentiment Analysis: A Comparative Study on Different Approaches☆
TL;DR: This paper compares the various techniques used for Sentiment Analysis by analyzing various methodologies and finds several methods for accomplishing this task to be superior.
Journal ArticleDOI
Unstructured Data Analysis on Big Data Using Map Reduce
TL;DR: The proposed method will process the data in parallel as small chunks in distributed clusters and aggregate all the data across clusters to obtain the final processed data.