Analyzing Social Media Sentiment: Twitter as a Case Study (original) (raw)

Twitter Sentimental Analysis using Deep Learning Techniques

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020

There is a rapid growth in the domain of opinion mining as well as sentiment analysis which targets to discover the text or opinions present on the disparate social media plat- forms via machine-learning (ML) with polarity calculations, sentiment analysis or subjectivity analysis. Sentimental analysis (SA) indicates the text organization which is employed to cate- gorize the expressed feelings or mindset in diverse manners like favorable, thumbs up, positive, unfavorable, thumbs down, negative, etc. SA is a demanding and notable task that compris- es i) natural-language processing (NLP), ii) web mining and iii) ML. Also, to tackle this challenge, the SA is merged with deep learning (DL) techniques since DL models are efficient because of their automatic learning ability. This paper emphasizes re- cent studies regarding the execution of DL models like i) deep neural networks (DNN), ii) deep-beliefnetwork (DBN), iii) convolutional neural networks (CNN) together with, iv) re- current neural network (RNN) model. Those DL models aid in resolving different issues of SA like a) sentiment classification, b) the classification methods of i) rule-based classifiers(RBC), KNN and iii) SVM classification methods. Lastly, the classi- fication methods’ performance is contrasted in respect of accu- racy.

Analyzing Sentiments of Twitter Trends Using Deep Learning

Most sentiment analysis techniques use the bag-of-words approach to determine sentiments, which ignore the sentence structure, making it oblivious to complex linguistic features like negations etc. This project takes a deep machine learning approach, using a new Recursive Neural Tensor Network(RNTN), to build a Sentiment Analysis Software as a Service(SaaS) backend server, which provides sentiment analysis of English texts using real time data extracted from Twitter. This RNTN model and is also able to capture the effect of negations in a sentence at different levels. This project also implements a web application which consumes the backend server to provide insightful information on Twitter trends in real time. The RNTN model is evaluated for accuracy using the Sentiment Treebank dataset, collected from movie reviews at rottemtomatoes.com. It obtains 80.22% accuracy on fine-grained sentiment prediction which is 5.4% above the state of art.

A STUDY ON TWITTER SENTIMENT ANALYSIS USING DEEP LEARNING

IRJET, 2023

Sentiment analysis is a branch of research that examines feelings, attitudes, and reviews from many public spheres. Now-a-days, people share their thoughts and insights on a wide range of issues and topics via social media. Recently social networking sites like Twitter and Face book have become popular because users can able to express their opinions with other internet users through micro blogging. Today Twitter is among the most widely used blogging sites. But the disrespectful, insensitive, or unfair remarks that occasionally appear in online forums drive many people away. The majority of websites are unable to promote productive discourse, thus either heavily restrict or fully disable user comments. Insightful data about what is stated on Twitter is provided when sentiment analysis is combined with Twitter. This study analyzed with various deep-learning techniques for the classification of negative and positive elements. Data set SemEval-2017 from Twitter is used to train the final models and will be useful to identify the model which produces the most accurate results.

Twitter Sentimental Analysis for Predicting Election Result using LSTM Neural Network

2019

In recent years, social media has emerged as a powerful widespread technology and caused a huge impact on the public debate and communication in the society. More recently, micro-blogging services (e.g., twitter) and social network sites are presumed to have the potential for increasing political participation. Among the most popular social media sites, Twitter serves as an ideal platform for users to share not only information in general but also political opinions publicly through their networks, political institutions have also begun to use this media for the purpose of entering into direct dialogs with citizens and encouraging more political discussions. So we build a model that can analyze these data and extract sentiment that can help us determine the outcome of the election. The process consists methods such as extraction of tweets from twitter using API, data cleaning to get exact data, training the LSTM (Long Short Term Memory) classifier using labelled dataset and testing ...

The social media sentiment analysis framework: deep learning for sentiment analysis on social media

International Journal of Electrical and Computer Engineering (IJECE), 2024

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.

Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

Future Internet, 2019

Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions’ features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets’ data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into norm...

Exploring Optimism and Pessimism in Twitter Using Deep Learning

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018

Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.

Twitter sentimental analysis using machine learning

2023

This research paper aims to explore the effectiveness of machine learning algorithms in analyzing sentiment on Twitter. The study utilizes a dataset of tweets collected from various sources, which were then preprocessed to remove noise and irrelevant data [4, 5]. To categorize the tweets as positive, negative, or neutral, a number of machine learning techniques were used, such as logistic regression and Naive Bayesian [1]. The efficiency of these algorithms is also assessed in the study using a number of criteria, including accuracy, precision, recall, and F1 score. The results indicate that machine learning algorithms are effective in analyzing sentiment on Twitter, with Naive Bayes providing the best performance [18]. The results of this study have significant ramifications for companies and organizations looking to track consumer opinion of their goods or services [7]. This paper examines the problem of analyzing sentiment in Twitter by examining the tweets' expressed sentiments-whether they be favourable, negative, or neutral. Natural language processing methods will be used to analyze the messages that are tweeted.

Assessing Security of Twitter by Sentimental Analysis using Deep Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

In order to make an approximation that is as accurate as feasible, sentiment analysis or opinion analysis is essential. Given that thoughtfully designed and carried out sentiment analysis can produce better and more accurate projections in both politics and business, this is a very essential component. At its most fundamental, feeling Analysis is based on the views that users and individuals share or express.There is a vast amount of material posted and exchanged over the internet every day by the users on various platforms. Understanding how different products, services, political figures, businesses, governments, and other entities are considered and perceived could be gained by being able to find the pattern in such data. This can handle a range of challenges, including being more reliable.Although we have numerous methods for sentiment analysis, a successful plan for regularly extracting and producing reliable sentiment analysis needs to be constructed. Despite the fact that machine learning algorithms have significantly improved-Naive Bayes, Support Vector Machine, and Maximum Entropy are the three that stand out as being particularly popular techniques for analysis, including good and bad views,is very much under study. I.

Sentiment Analysis Of Twitter Data By Using Deep Learning And Machine Learning

Turkish Journal of Computer and Mathematics Education (TURCOMAT)

In today’s world, social media is viral and easily accessible. The Social media sites like Twitter, Facebook, Tumblr, etc. are a primary and valuable source of information.Twitter is a micro-blogging platform, and it provides an enormous amount of data. Such type of information can use for different sentiment analysis applications such as reviews, predictions, elections, marketing, etc. It is one of the most popular sites where peoples write tweets, retweets, and interact daily. Monitoring and analyzing these tweets give valuable feedback to users. Due to this data's large size, sentiment analysis is using to analyze this data without going through millions of tweets manually. Any user writes their reviews about different products, topics, or events on Twitter, called tweets and retweets. People also use emojis such as happy, sad, and neutral in expressing their emotions, so these sites contain expansive volumes of unprocessed data called raw data. The main goal of this research...