Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

TWEEZER – Tweets Analysis

Twitter is one in all the foremost used applications by the people to precise their opinion and show their sentiments towards different occasions. Sentiment analysis is an approach to retrieve the sentiment through the tweets of the general public. Twitter sentiment analysis is application for sentiment analysis of information which are extracted from the twitter(tweets). With the assistance of twitter people get opinion about several things round the nation .Twitter is one such online social networking website where people post their views regarding to trending topics .It s huge platform having over 317 million users registered from everywhere the globe. a decent sentimental analysis of information of this huge platform can result in achieve many new applications like – Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc. during this paper, we used two specific algorithm –Naïve Bayes Classifier Algorithm for polarity Classification & Hashtag classification for top modeling. this system individually has some limitations for Sentiment analysis. The goal of this report is to relinquish an introduction to the present fascinating problem and to present a framework which is able to perform sentiment analysis on online mobile reviews by associating modified naïve bayes means algorithm with Naïve bayes classification

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

NEW MODERN APPROACH TO PREDICT USERS SENTIMENT USING CNN AND BLSTM

In Today’s world social network play a vital role and provides relevant information on user opinion. This paper presents emotional health monitoring system to detect stress and the user mood. Depending on results the system will send happy, calm, relaxing or motivational messages to users with phycological disturbance. It also sends warning messages to authorized persons in case a depression disturbance is detected by monitoring system. This detection of sentence is performed through convolution neural network (CNN) and bi-directional long-term memory (BLSTM). This method reaches accuracy of 0.80 to detect depressed and stress users and also system consumes low memory, process and energy. We can do the future work of this project by also including the sarcastic sentences in the dataset. We can also predict the sarcastic data with the proposed algorith

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses di erent neural network-based approaches for analyzing sentiment. Finally, these di erent approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.Ministerio de Educación (MOE) en Taiwán N/

A Deep Learning based Model using Review Associated Feature Extraction Approach for Sentiment Analysis

With the advancement of internet technologies, in the present days, online forums, social media platforms and e-commerce sites have made the product reviews process very easy. There are a lot of mobile applications, websites and forums where consumers used to share and circulate their opinions, experiences, ideas and views regarding products, brands and services. In consequence, online user reviews have become a deciding factor for many consumers prior to purchasing their selected items. The sentiment analysis is a technique to extract sentiments, feelings and insights from customer reviews and public texts. Therefore, plenty of businesses perform sentiment analysis in order to more thoroughly comprehend of their customer opinions and suggestions regarding their products and services. Furthermore, a number of scientific researchers also have a keen interest in classifying customer reviews into a set of labels employing text classification techniques. The objective of the this research work is to develop an approach to extract review associated features using Part-of-Speech (POS) tagging and design a CNN model to classify the reviews' sentiment as positive or negative. In this paper, an approach to extract review associated feature has been presented. Natural Language Processing (NLP) techniques are utilized for data preprocessing to remove uninformative data from reviews. Deep learning model CNN is used for sentiment classification and Amazon mobile reviews dataset is used for the experiment. The proposed model is experimentally evaluated and provides enhanced performance than other models also provides improved accuracy of 97.23% on Amazon mobile review dataset

Unveiling the Emotional and Psychological States of Instagram Users: A Deep Learning Approach to Mental Health Analysis

People can now communicate with others who have common tastes to them and engage in conversation together while furthermore exchanging ideas, photos, and clips that convey their emotional states due to social media’s technology. As a consequence, there is an opportunity to investigate person sentiments and thoughts in social networking sites data in order to understand their viewpoints and sentiments when utilizing these digital platforms for interaction. Utilizing social network data to diagnose depression has gained extensive acceptance, there is still a number of unidentified characteristics. Due to its potential to shed light on the forecasting model, model complexity is crucial for facilitating communication. For example, the majority of algorithms for machine learning produce results in the automatic depression forecasting test that are challenging for people to understand. In this research the mental health condition is analyzed using deep learning approach by considering the data from Instagram data. In this investigation, researchers created the Hybrid deep learning approach, which divided the sentiment ratings into different categories: Neutral, Positive, Negative. Researchers also contrasted the performance of the recommended approach with other machine learning algorithm on a number of criteria, including accuracy, sensitivity, F1 score, and precision