Sentiment Analysis using Optimized Feature Sets in Different Facebook/Twitter Dataset Domains using Big Data

Mohammed Ibrahim Al-Mashhadani Mohammed Ibrahim Al-Mashhadani,Muhammad Ilyas Muhammad Ilyas,Enas Tariq Khudir Enas Tariq Khudir,Kilan M Hussein Kilan M Hussein

doi:10.52866/ijcsm.2022.01.01.007

Mohammed Ibrahim Al-Mashhadani Mohammed Ibrahim Al-Mashhadani, Muhammad Ilyas Muhammad Ilyas + Show 2 more

Open Access

https://doi.org/10.52866/ijcsm.2022.01.01.007

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Abstract

Now days, in many real life applications, the sentiment analysis plays very vital role for automatic prediction of human being activities especially on online social networks (OSNs). Therefore since from last decade, the research on opinion mining and sentiment analysis is growing with increasing volume of online reviews available over the social media networks like Facebook OSNs. Sentiment analysis falls under the data mining domain research problem. Sentiment analysis is kind of text mining process used to determine the subjective attitude like sentiment from the written texts and hence becoming the main research interest in domain of natural language processing and data mining. The main task in sentiment analysis is classifying human sentiment with objective of classifying the sentiment or emotion of end users for their specific text on OSNs. There are number of research methods designed already for sentiment analysis. There are many factors like accuracy, efficiency, speed etc. used to evaluate the effectiveness of sentiment analysis methods. The MapReduce framework under the domain of big-data is used to minimize the speed of execution and efficiency recently with many data mining methods. The sentiment analysis for Facebook OSNs messages is very challenging tasks as compared to other sentiment analysis because of misspellings and slang words presence in twitter dataset. In this paper, different solutions recently presented are discussed in detail. Then proposed the new approach for sentiment analysis based on hybrid features extraction methods and multi-class Support Vector Machine (SVM). These algorithms are designed using the Big-data techniques to optimize the performance of sentiment analysis

Highlights

Online social network (OSN) websites, such as Facebook and Twitter, are common nowadays; they have allowed individuals to express their opinions, share their thoughts or report real-time events at ever-increasing rates
Nowadays, in many real-life applications, sentiment analysis plays a vital role in the automatic prediction of human activities, especially on online social networks (OSNs)
The research on opinion mining and sentiment analysis has been growing with the increase in volume of online reviews available over the social media networks, such as Facebook OSNs

Summary

INTRODUCTION

Online social network (OSN) websites, such as Facebook and Twitter, are common nowadays; they have allowed individuals to express their opinions, share their thoughts or report real-time events at ever-increasing rates. Even state-of-the-art methods of opinion mining and sentiment analysis tend to have a low performance when analysing OSN data [6]. The sentiment analysis process discussed in this study originates from the data mining domain whose common steps include pre-processing (e.g. noise removal), feature extraction (e.g. positive and negative features) and classification (e.g. SVM and Naïve Bayes). The volume of tweets or online messages in OSNs are continuously increasing Analytic methods, such as performance-efficient sentiment classification, are required to be able to cope with the large volume of OSN messages [10]. This research aims to present the recent existing techniques of sentiment analysis along with their comparative analytic studies.

LITERATURE REVIEW

ANALYSIS

Methodology

RESEARCH GAPS

EFFICIENCY OF THE METHOD

CONCLUSION AND FUTURE WORK