Open AccessAspect Based Sentiments from Tweets using Co-Ranking Multi-Modal Natural Language Processing Methodologies
Author(s) -
M. Kanipriya*,
R. Krishnaveni,
Madan Krishnamurthy,
S. Bairavel
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6305.018520
Subject(s) - computer science , sentiment analysis , ranking (information retrieval) , event (particle physics) , parsing , social media , natural language processing , artificial intelligence , stop words , modal , process (computing) , information retrieval , natural language , world wide web , chemistry , physics , quantum mechanics , preprocessor , polymer chemistry , operating system
Now-a-days people interest to spend their time in social sites especially twitters to post lot of tweets in every day. The posted tweets are used by many users to get the knowledge about the particular applications, products and other search engine queries. With the help of the posted tweets, their emotions and sentiments are derived which are used to get opinion about particular event. Lot of traditional sentiment detection system that has been developed but they failed to analyze huge volume of tweets and online contents with temporal patterns were also difficult to analyze. To overcome the above issues, the co-ranking multi-modal natural language processing based sentiment analysis system was developed to detect the emotions from the posted tweets. Initially, tweets of different events are collected from social sites which are processed by natural language procedures such as Stemming, Lemmatization, Part-of-speech tagging, word segmentation and parsing are applied to get the words related to posted tweets for deriving the sentiments. From the extracted emotions, co-ranking process is applied to get the opinion effectively related to particular event. Then the efficiency of the system is examined using experimental results and discussions. The introduced system recognize the sentiments from tweets with 98.80% of accuracy.