Machine Learning Prospects in Social Media and Cloud Data Mining and Analytics (original) (raw)
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Analysis of Twitter Data Using Machine Learning Algorithms
EPRA international journal of research & development, 2023
Sentiment analysis is one among the distinguished fields of knowledge and pattern mining that deals with the identification and analysis of sentiment within the text. The main challenges in sentiment analysis are word ambiguity and multi polarity. The problem of word ambiguity is to define polarity because the polarity for words is context dependent. The tweets are initially preprocessed. The preprocessing includes the removal of stop words, and lower case conversion. The tweets are then passed to the feature extraction techniques. Then the data is splitted as training and testing data. The trained data is passed to the different machine learning algorithm like Naive Bayes. Support Vector machine, Random forest, and Decision Tree and k-NN algorithm. The accuracy obtained using the Naive Bayes. Support Vector machine, random forest, and Decision Tree, k-NN and Logistic regression algorithm is 80%, 77%, 72%, 61% ,56% and 78%. The naïve bayes algorithm has achieved a better accuracy when compared to the other algorithm.
Machine Learning Approach to Sentiment Analysis in Data Mining
Passer Journal, 2022
Widespread internet use and the web have brought about new ways of expressing individual sentiments. A sentiment is defined as an individual's view in which feelings, attitudes, and thoughts can be represented. When it comes to analysing and extracting Sentiment analysis and opinion mining are two of the most prominent disciplines of research. They derive insights using text data through numerous sources like Facebook and Twitter. Sentiment analysis frequently elicits information on how people feel about various events, brands, products, or businesses. Researchers collect and improvise replies from the general public to conduct evaluations. This paper looks into sentiment analysis for classifying Twitter subscriber tweets. This approach can help analysing the information gathered and stored in positive, neutral and negative opinions. This information is first pre-processed before creating feature vectors. On the basis of machine learning, classification methods were used. The study's algorithms are used Maximum Entropy, Naive Bayes and Support Vector Machine; they are used to categorize documents as positive or negative. The dataset for this paper are obtained from Twitter and includes subscribed tweets by using the API. Following pre-processing, machine learning methods are used to determine whether the tweets are positive or negative.
Twitter Sentiment Analysis using Machine Learning Algorithms: A Comparative Analysis
Soft Computing Research Society eBooks, 2024
Data posted by people, or the users of a particular social network, has increased dramatically due to the changing behavior of various social networking sites like Instagram, Twitter, Snapchat, etc. Innumerable millions and billions of bytes of audio, video, and text are uploaded daily. This is because millions of people use a particular website. These folks are interested in sharing their thoughts and opinions on any topic they choose. People also want to know if most people will see an incident favorably, unfavorably, or neutrally. In this paper, the data is classified into Positive, Negative, or Neutral opinions, and it presents a detailed survey of Sentiment analysis of Twitter data using various Machine learning algorithms like Naïve Bayes, Support Vector Machine (SVM), Logistic regression, and decision tree. Additionally, the accuracy and F1 scores of the aforementioned algorithms are examined on two distinct Twitter datasets, and a comparison is made between the algorithms respective accuracies in the two datasets.
International Journal of Information Technology and Computer Science
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 organization 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 sa...
Social Media Mining Using Machine Learning Techniques as a Survey
Advances in computer science research, 2023
In today's world an online existence and social media users utilize various social media platforms to express or comments their observations and opinions. The role of social media platforms are predicting Government Initiatives, Election results, product Analysis, business analysis, movie popularity, sports outcomes and stock market analysis. This review paper proposed the opinions are expressed through different social media platforms can be used for retrieving or extracting the real time predictions on several trends. As per the sentiment identification outcome find the features in the form of Positive (+ve), Negative (−ve) and Neutral (=). In this proposed research methodology, here collect user's reviews on particular trends, then preprocessed it, creation of the features and selecting for data classification using different machine leering classifiers and predict the result. For better performance, used advanced preprocessing techniques will be applied to cleaning the data. For Sentiment Classification will be used machine learning algorithms or techniques like (SVM) Support Vector machine, (ME) Maximum Entropy, (NB) Naïve Bayes and (DT) Decision tree. As per existing techniques, It is very difficult to mine the correct predictions from social media. Therefore, the prediction model will be designed for doing the prediction using real time data from Twitter. An opinion from text or comment posted on social media platforms by various categories of users is one of the critical and time consuming tasks in the field of opining mining and analysis. The importance of this proposed intelligent system for social media is to automatically providing polarity from unstructured data in the form of text in English language for effective decision making.
Sentiment Analysis Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Sentiment analysis falls within the category of analytics research. This can make sense by reading raw data using computational methods. This is what analysis is. Written expressions that are neutral, unfavourable, or indifferent can be assessed using sentiment analysis. People use a variety of social media platforms, including Facebook and Twitter, which is a useful tool for gauging public sentiment. This uses a variety of machine learning techniques. We have considered a variety of sentiment analysis techniques in this study. Using machine learning classifiers, sentiment analysis has been carried out. Users' tweets are categorised as having "positive" or "negative" sentiment using polarity-based sentiment analysis and deep learning models. Sentiment Analysis, one of the branches of computer science that is now gaining the most ground. I.
Machine learning-based technique for big data sentiments extraction
IAES International Journal of Artificial Intelligence (IJ-AI)
A huge amount of data is generated every minute for social networking and content sharing via Social media sites that can be in a form of structured, unstructured or semi-structured data. One of the largest used social media sites is Twitter, where each and every day millions of data generated in the form of unstructured tweets. Tweets or opinions of the people can be used to extract sentiments of the people. Sentiment analysis is beneficial for organizations to improve their products and make required changes on demand to increase their profit. In this paper, three machine learning algorithms Support Vector Machine (SVM), Decision Trees (DT), and Naive Bayes (NB) for classifying sentiments of twitters data. The purpose of this research is to compare the outcomes of these algorithms to identify best machine learning method which gives most accurate and efficient results for classifying twitter data. Our experimental result shows that same preprocessing methods on a different datase...
International Journal of Advanced Science Computing and Engineering, 2024
Social media platforms, particularly Twitter, have become essential sources of data for various applications, including marketing and customer service. This study focuses on analyzing customer interactions with Amazon's official support account, "@AmazonHelp," to understand and predict changes in customer sentiment during these interactions. Using the Twitter API, we extracted English-language tweets mentioning "@AmazonHelp," pre-processed the data, and categorized conversations to facilitate analysis. The primary objectives were to classify changes in customer sentiment and predict the overall sentiment change based on initial sentiment. We conducted experiments using multiple machines learning algorithms, including K-nearest neighbor, Naive Bayes, Artificial Neural Network, Bayes Net, Support Vector Machine, Logistic Regression, and Bagging with RepTree. Our dataset comprised over 6,500 conversations, filtered to include those with four or more tweets. Results indicated that K-nearest neighbor and Support Vector Machine offered the best balance between accuracy and F-measure, while Bagging with RepTree achieved the highest accuracy but had a lower F-measure. This study demonstrates the potential of integrating sentiment analysis and machine learning to effectively predict customer sentiment in social networks, providing valuable insights for improving customer engagement strategies.
Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).
Machine Learning-Based Sentiment Analysis for Twitter Accounts
Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM).