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Sentiment Analysis on Social media

Anumula Manjula, Dr. A. Rama Mohan Reddy, , ,
Affiliations
Computer Science and Engineering, Sri Venkatewsara University College of Engineering, Sri Venkateswara University, Tirupati, India
:10.22362/ijcert/2019/v6/i12/v6i12


Abstract
The detailed work done in developing a system used for opinion analysis of a product or a service. The system readily processes the tweets by pulling data from twitter posts, pre-processing it and connecting it to Twitter API by REST call method and showing it graphically. We have given the analysis for the public tweets by API and filters them for various products , persons and services. For written product reviews, the best solution is video review. Collecting comments from YouTube videos and extracting the exact tone or behavior behind it. The most widely used approaches in opinion mining focus only on tweets or written product reviews available on websites like Amazon. Various emotions that can deal here namely Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Curiosity, Excitement, Gratitude, Serenity, Hope, Pride, Amusement, Jealousy, Guilt, Discouragement, Frustration, Rejection, Disappointment, Loneliness, Interest, lack of interest, Concern, Sympathy and Calm. Online news is also now trending and extracting the proper tone behind the news. The analysis which is used to classify the sentiment as positive, negative, neutral, strong positive, weak positive, strong negative, and weak negative. The results shown textually and graphically.


Citation
Anumula Manjula,Dr. A. Rama Mohan Reddy."Sentiment Analysis on Social media". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084,Vol.6, Issue 11, pp.1-6,November - 2019,


Keywords : Sentiment analysis, Twitter, YouTube, Online-News, data, analysis

References
[1] Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Pado, and Roman Klinger,” “Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus ” by” , Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 13-23, Copenhagen, Denmark, September 7-11, 2017.

[2] Sunidhi Sharma, D.K.Sharma, Supriti Sharma, “Text Analysis and Sentiment Analysis using Facebook in R Language: Case studies” , International Journal of Computer and Mathematical Sciences, ISSN 2347-8527, vol 6 Issue 12, December 2017.

[3]	Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo, “Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark,  September 7-11, 2017.

[4]	Kishaloy Halder, Lahari Poddar, Min-Yen Kan, “Modeling Temporal Progression of Emotional Status in Mental Health Forum : a Recurrent Neural Net Approach” Proceedings of Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 127-135, Copenhagen, Denmark, September 7-11, 2017.

[5]	Jeremy Barnes, Roman Klinger, and Sabine Schulte im Walde, “Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets”  Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 2-12, Copenhagen, Denmark, September 7-11, 2017.

[6]	Keenen Cates, Pengcheng Xiao, *, Zeyhang, Calvin Dailey, “Can Emoticons Be Used To Predict Sentiment?”  Journal of Data Science 355-376, April 04, 2018.

[7]	Prabaharan Poornachandran,, “deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets”  Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark, September 7-11, 2017.


DOI Link : https://doi.org/10.22362/ijcert/2019/v6/i12/v6i12

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