Twitter sentiment analysis, 3-way classification: positive, negative or neutral?
Çeliktuğ, Mestan Fırat
Proceedings of the 2018 IEEE International Conference on Big Data, Big Data 2018
Institute of Electrical and Electronics Engineers Inc.
2098 - 2103
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People face with the huge amount of information on each day with the advent of big data era. The data amount stored and processed by Facebook, Twitter and other big social networks store (e.g. Instagram) is massive in those days. Online social networks provide great opportunity for propagation of almost any type of information. It's actually much much easier to disseminate an idea/knowledge than previous times. Naturally, this creates information validity and immediate curiosity about mass evaluation problem in general. In this regard, sentimental polarity detection in social media (e.g.Classification of a tweet as negative or positive or neutral) is highly valuable for certain institutions, organizations. The study's main focus is to classify negative, positive and neutral approaches of three (3) annotated twitter datasets. Effect of oversampling, unigram features and other features on overall and class-based accuracy ratios is worked on the datasets. Baseline is reached in dataset-2 experiments. 88% overall accuracy was observed in dataset-1 experiments which outperforms the prior art.Unigram features has shown significant effect on overall accuracy, class-based accuracy balance.
Keywords3-way sentiment analysis
Social media analysis
Twitter sentiment analysis
Published Version (Please cite this version)https://dx.doi.org/10.1109/BigData.2018.8621970
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