Author(s):
1. Misbah Iram:
University Institute of Information Technology, University of Arid Agriculture,Rawalpindi,Pakistan
2. Saif Ur Rehman:
University Institute of Information Technology, University of Arid Agriculture,Rawalpindi,Pakistan
3. Shafaq Shahid:
University Institute of Information Technology, University of Arid Agriculture,Rawalpindi,Pakistan
4. Sayeda Ambreen Mehmood:
University Institute of Information Technology, University of Arid Agriculture,Rawalpindi,Pakistan
Abstract:
Sentiment Analysis (SA) is an eficient way of determining people's opinions from a piece of text. SA using diferent social media sites such as Twitter has achieved tremendous results. Twitter is an online social media platform that contains a massive amount of data. The platform is known as an information channel corresponding to diferent sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which are important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment on Twitter have been discussed. There has been an extensive research studies in the field of SA of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable opinion mining techniques based on machine learning and lexicon-based along with their metrics. The proposed work is helpful in informaiton analysis in the tweets where opinions are found heterogeneous, unstructured, polarised negative, positive, or neutral. In order to validate the supremacy of the suggested approach, we have executed a series of experiments on the real-world Twitter dataset that alters to show the efectiveness of the proposed framework. This research efort also highlighted the recent challenges in the SA field and the proposed work's future scope. .
Page(s):
63-75
DOI:
DOI not available
Published:
Journal: Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, Volume: 59, Issue: 2, Year: 2022
Keywords:
machine learning
,
twitter
,
Sentiment analysis
,
Social Media
,
Opinion Mining
,
Sentiment Aspects Extraction
,
Social Network Analysis
References:
References are not available for this document.
Citations
Citations are not available for this document.