Visible to the public Twitter Opinion Mining and Boosting Using Sentiment Analysis

TitleTwitter Opinion Mining and Boosting Using Sentiment Analysis
Publication TypeConference Paper
Year of Publication2018
AuthorsGeetha, R, Rekha, Pasupuleti, Karthika, S
Conference Name2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP)
ISBN Number978-1-5386-1141-8
KeywordsCameras, data mining, emotions, exclusive entities, Gold, Human Behavior, Meta-level, natural language processing, natural language texts, opinion mining, opinions, pattern classification, personal mood, pubcrawl, public conviction, public opinion, resilience, Resiliency, Scalability, sentiment analysis, sentiment proportions, sentiments, Signal processing, SNLP, social media, social networking (online), Synset, Task Analysis, Twitter data, twitter sentiment classification

Social media has been one of the most efficacious and precise by speakers of public opinion. A strategy which sanctions the utilization and illustration of twitter data to conclude public conviction is discussed in this paper. Sentiments on exclusive entities with diverse strengths and intenseness are stated by public, where these sentiments are strenuously cognate to their personal mood and emotions. To examine the sentiments from natural language texts, addressing various opinions, a lot of methods and lexical resources have been propounded. A path for boosting twitter sentiment classification using various sentiment proportions as meta-level features has been proposed by this article. Analysis of tweets was done on the product iPhone 6.

Citation Keygeetha_twitter_2018