The method of Lexicons applied to Sentiment Analysis on Twitter Data to address NLP issues
Keywords:
Tweeter, sentiment analysis, twitter data analysis, NLPAbstract
As the world grows, social networks are one of the biggest sources of information where a lot of people interact and communicate. Among the various social media platforms Twitter is one type of social media that is often used. Users tweet their thoughts to the public. Sharing and taking of reviews has been a helpful way to learn about opinions about things. These opinions can lead to Sentiment Analysis, in Twitter there are tweets that can be sentiments. It can be defined as policy, logic, etc. Firstly, a natural language processing-based pre-processes data framework is created to filter tweets. Tokenization sentiment is a technique to be used as a stemming technique. Overall, the process is intended to ascertain what a person thought about a specific tweet they posted. A bag of Words is incorporated to frame a model concept to analyse sentiment. Using this technique, positive and negative tweets can be classified, and also lexicons are used for developing sentiment analysis of tweets.References
Tiwari, S., Verma, A., Garg, P., & Bansal, D. (2020, March). Social media sentiment analysis on Twitter datasets. In 2020 6th international conference on advanced computing and communication systems (ICACCS) (pp. 925-927). IEEE.
Rosenthal, S., Farra, N., & Nakov, P. (2019). SemEval-2017 task 4: Sentiment analysis in Twitter. arXiv preprint arXiv:1912.00741.
Ahmed, S. S. T., Thanuja, K., Guptha, N. S., & Narasimha, S. (2016, January). Telemedicine approach for remote patient monitoring system using smart phones with an economical hardware kit. In 2016 international conference on computing technologies and intelligent data engineering (ICCTIDE'16) (pp. 1-4). IEEE.
Ahmed, K., El Tazi, N., & Hossny, A. H. (2015, October). Sentiment analysis over social networks: an overview. In 2015 IEEE international conference on systems, man, and cybernetics (pp. 2174-2179). IEEE.
Nagashree, N., Patil, P., Patil, S., & Kokatanur, M. (2019). Performance metrics for segmentation algorithms in brain MRI for early detection of autism. Int. J. Innovative Technol. Exploring Eng.(IJITEE), 9.
Nagesh, N., Patil, P., Patil, S., & Kokatanur, M. (2022). An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm. International Journal of Electrical & Computer Engineering (2088-8708), 12(2).
Ahmed, S. T., Singh, D. K., Basha, S. M., Abouel Nasr, E., Kamrani, A. K., & Aboudaif, M. K. (2021). Neural network based mental depression identification and sentiments classification technique from speech signals: A COVID-19 Focused Pandemic Study. Frontiers in public health, 1926.
Sreedhar Kumar, S., Ahmed, S. T., & NishaBhai, V. B. Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique. International Journal of Recent Technology and Engineering (IJRTE), 8(10).
Al-Shammari, N. K., Alzamil, A. A., Albadarn, M., Ahmed, S. A., Syed, M. B., Alshammari, A. S., & Gabr, A. M. (2021). Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN. Engineering, Technology & Applied Science Research, 11(4), 7436-7441.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 M Raghavi, Nagashree N
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
CC Attribution-NonCommercial-NoDerivatives 4.0
