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13 articles for “Tweet classification”
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Tweets Classification using Different Classifiers for Sentiment Analysis
Abstract: Twitter is a famous platform for social networking where people share and engage with "tweets" comments. It is a way of expressing people's opinions or emotions on various topics. Different parties, like producers and consumers, have carried out a sentiment analysis on tweets to gain insights into goods or to analyze the sector. Also, the accuracy of our sentiment analysis forecasts will increase with the recent advances in machine learning …
Published in Research & Reviews : Journal of Statistics · Vol. 10, Issue 3, 2021 · pp. 34–43 Read article
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Opinions mining of Twitter events using spatial-temporal features
Abstract: Micro-blogs are a powerful tool to express an opinion. Twitter is one of the fastest growing micro-blog and has more than 900 million users. Twitter is a rich source of opinion as users share their daily experience of life and respond to specific events using tweets on twitter. In this paper, an automatic opinion classifier capable of automatically classify tweets into different opinion expressed by them is developed. Also manually …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 2, 2018 · pp. 36–44 Read article
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Hybrid approach for emotion detection in text using spatial-temporal features
Abstract: In recent years, the incredible exploratory growth of online data in social media mainly Twitter has to lead to growing attention of researchers towards Affective analysis of social media streams. Around 500 million tweets are generated per day all around the globe. Classification of these tweets into different affective classes is an arduous task. We propose a novel approach of classifying a tweet into Ekman’s six basic emotion classes using …
Published in Journal of Advancements in Robotics · Vol. 5, Issue 1, 2018 · pp. 34–44 Read article
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A Comprehensive Method for Sentiment Analysis on Twitter Posts
Abstract: AbstractIn recent years, with ascent of users of social media, researchers get drawn to sentiment analysis. The challenge is that sentiment-dependent info from multiple sources does not seem to be thought of usually in existing sentiment classification techniques. Sentiment analysis is incredibly vital as a result of it helps folks and organizations in their decision-making process. There is vast explosion of “sentiments” on the market from social media like Twitter, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 4, Issue 2, 2017 · pp. 1–5 Read article
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Sentiment Analysis of Twitter Data for Better Services of Airlines Using Classifier-Model Algorithms, AI, and NLP Techniques
Abstract: The rising attractiveness of social media sites and usage of social websites is to share information. Tweet, for instance, is a stage in which audiences direct, and examine posts recognized as “tweets,” and engage together in one-of-a-kind communities through feedback, comments, tweets, and reviews. There are so many companies that want tweets, comments, reviews, and analyses on their products and services, so they can improve their services, products, and customer …
Published in Current Trends in Information Technology · Vol. 12, Issue 3, 2022 · pp. 29–46 Read article
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Evaluating the Efficiency of LLMs-SA (Sentiment Analysis) via Social Media Texts
Abstract: Sentiment analysis (SA) is becoming popular in business and scientific communities as the processing of natural language (NLP), computational linguistics, text analytics, image-based processing or video- based processing is used in extracting and mining subjective information in the web, social network, etc. It is able to detect positive, negative or neutral information and can be selected to absorb polarity, sentiments, urgency and goals of mount importance. The majority of the …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Real Time Analysis of Twitter Data
Abstract: In last two decades, big data has emerged as one of the most trending fields of research. In today’s world, every minute, huge amount of structured and unstructured data is produced via various platforms. This data includes texts, images, and forums gathered from social media platforms. This increasing data implies a fundamental change in the way we analyze data since it is unable to analyze data using conventional methodologies and …
Published in Current Trends in Information Technology · Vol. 6, Issue 1, 2016 · pp. 6–14 Read article
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Evaluation of Machine Learning Classifiers for Sentiment Analysis
Abstract: Sentiment in social media refers to users’ emotions and opinions through their posts and interactions. Sentiment analysis (SA) refers to relating and classifying the sentiments expressed as engagement and interactions between users. When analyzed, tweets frequently produce a large source of clustered data. These data help determine people’s opinions about a variety of motifs. Thus, this study presents an Automated Machine Learning (ML) Sentiment Analysis Model to detect media sentiment. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 141–154 Read article
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An Unsupervised Common Sense-based Learning Framework for Emotion Detection and Classification in Textual Social Data
Abstract: AbstractEmotions are inherent to human behavior and generally expressed in response to the stimuli from the external environment or some internal processes. With the widespread use of online social platforms, people often express themselves using tweets, blogs and posts which generally include affective text. The topic of emotion detection and recognition has been widely studied in computational science and in fact a separate area “Affective Computing” is dedicated to its …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 4, Issue 3, 2017 · pp. 44–51 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article
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Perception Analysis of Social Network Tweets Using a Genetic Algorithm Based Approach
Abstract: We communicate, post different comments, communicating with different people in Social media. Different people of the world posted their views on COVID-19 during the pandemic. In this study we analysed the posted comments on social media. Analysis of the comment in social media is called sentiment analysis. We performed sentiment analysis on content from social media platforms, classifying it into either positive or negative categories. For this task, we proposed …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 10, Issue 2, 2023 · pp. 27–38 Read article
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Feature Selection and Weighted Based Optimized Weight based Multi-Tier Stacked Ensemble (WMTSE) Classification for Twitter Sentiment Analysis
Abstract: AbstractThis research work concentrates on both feature selection and classification methods for utilizing twitter data. A new classifier is introduced for classifying “tweets” into positive, negative and neutral sentiment. The system contains four steps: Preprocessing by Tokenization, Text Cleaning, Part of Speech (PoS) Tagging, Stemming and Stop Words Removal, Feature Extraction by Bag-of-words (BoW), Lexicon-based features and Term Frequency- Inverse Document Frequency(TF-IDF), Feature Selection by Binary Swallow Swarm Optimization (BSSO) …
Published in Journal Of Network security · Vol. 8, Issue 3, 2020 · pp. 20–23 Read article
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Sentiment Analysis Based Telecom Churn Prediction
Abstract: Predicting customer churn is a big challenge and a survival basic for Telecom operators. In a large and competitive market like India, it is very essential to gather real-time customer feedback as a health indicator. Social networks have evolved as a rich source of real-time sentiments and opinions of the general public. In this research, tweets for the Twitter handle of 5 major telecom brands in India: Aircel, Bharti Airtel, …
Published in Journal of Web Engineering & Technology · Vol. 7, Issue 1, 2020 · pp. 6–12 Read article