Location based popularity analysis of Twitter data

10.32920/ryerson.14668347.v1 ◽

2021 ◽

Author(s):

S.M. Rashel Rana

Keyword(s):

User Interface ◽

Sentiment Analysis ◽

Significant Contribution ◽

Research Work ◽

Twitter Data

This research aims to analyze location based twitter data to measure the popularity of the products/persons or any given user parameter. For this purpose this work has integrated sentimental analysis, location based system and ontology. An application with a novel user interface has been developed to search and visualize the data on Google map. This research work uses publicly available and location enabled twitter data. This work also has the capability to process tweet data without user’s locations. The main contribution of this research is the integration of sentimental analysis on location based Twitter data. Another significant contribution is the development of a novel user interface, which allows the user to search on a map interactively with multi-focusing features on Google map. This integrated sentiment analysis work efficiently performs location based popularity scaling on products, persons, brands or any given topic.

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Sentiment Analysis on Twitter Data: A Comparative Approach

International Journal of Computer Science and Mobile Applications ◽

10.47760/ijcsma.2021.v09i10.001 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1-12

Author(s):

Subhadip Chandra ◽

Randrita Sarkar ◽

Sayon Islam ◽

Soham Nandi ◽

Avishto Banerjee ◽

...

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Computational Linguistics ◽

Language Processing ◽

Research Work ◽

Comparative Approach ◽

Support Vector ◽

Affective States ◽

Twitter Data ◽

Social Media Platforms

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), Naïve Bayes, Maximum entropy are used to analyse Twitter data. Neural Network (NN), Decision tree-based sentiment analysis is also covered in this research work, to find out better accuracy of the approaches in the various data range. Graphs and confusion matrices are used to visualise the results of the analysis for positive, negative, and neutral remarks regarding their opinions.

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Hybrid Classification Technique for Sentiment Analysis of the Twitter Data

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9116.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1750-1753

Keyword(s):

Social Media ◽

Random Forest ◽

Sentiment Analysis ◽

Research Work ◽

Related Data ◽

Twitter Data ◽

Opinion Extraction ◽

Social Media Platforms ◽

Hybrid Classification ◽

Media Platform

Sentiment can be described in the form of any type of approach, thought or verdict which results because of the occurrence of certain emotions. This approach is also known as opinion extraction. In this approach, emotions of different peoples with respect to meticulous rudiments are investigated. For the attainment of opinion related data, social media platforms are the best origins. Twitter may be recognized as a social media platform which is socially accessible to numerous followers. When these followers post some message on twitter, then this is recognized as tweet. The sentiment of twitter data can be analyzed with the feature extraction and classification approach. The hybrid classification is designed in this work which is the combination of KNN and random forest. The KNN classifier extract features of the dataset and random forest will classify data. The approach of hybrid classification is applied in this research work for the sentiment analysis. The performance of the proposed model is tested in terms of accuracy and execution time.

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Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data

International Journal of Information Technology and Computer Science ◽

10.5815/ijitcs.2021.02.04 ◽

2021 ◽

Vol 13 (2) ◽

pp. 38-48

Author(s):

Golam Mostafa ◽

◽

Ikhtiar Ahmed ◽

Masum Shah Junayed

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Personal Development ◽

Data Science ◽

Research Work ◽

Machine Learning Algorithms ◽

Learning Classifier ◽

Twitter Data ◽

Classifier Algorithms

In recent years, with the advancement of the internet, social media is a promising platform to explore what going on around the world, sharing opinions and personal development. Now, Sentiment analysis, also known as text mining is widely used in the data science sector. It is an analysis of textual data that describes subjective information available in the source and allows an rganization to identify the thoughts and feelings of their brand or goods or services while monitoring conversations and reviews online. Sentiment analysis of Twitter data is a very popular research work nowadays. Twitter is that kind of social media where many users express their opinion and feelings through small tweets and different machine learning classifier algorithms can be used to analyze those tweets. In this paper, some selected machine learning classifier algorithms were applied on crawled Twitter data after applying different types of preprocessors and encoding techniques, which ended up with satisfying accuracy. Later a comparison between the achieved accuracies was showed. Experimental evaluations show that the Neural Network Classifier’algorithm provides a remarkable accuracy of 81.33% compared with other classifiers.

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