scholarly journals Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets

2018 ◽  
pp. 1-9 ◽  
Author(s):  
AYTUĞ ONAN
Keyword(s):  
Sentiment Analysis ◽  
Linguistic Feature ◽  
Feature Sets

scholarly journals Sentiment Analysis using Optimized Feature Sets in Different Twitter Dataset Domains

2019 ◽  
Vol 8 (11) ◽  
pp. 3035-3039
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Support Vector ◽  
Text Analytics ◽  
K Nearest Neighbors ◽  
Verbal Language ◽  
Feature Sets ◽  
Goods And Services ◽  
Assessment Metrics

in the last years, the relevance of sentiment analysis is broad and dominant. The capability to take out insights from social data is a tradition that is being extensively accepted by all over globe. Sentiment Analysis has turn out to be a hot-trend issue of technical and marketplace research in the area of Natural Language Processing (NLP) and Machine Learning. Sentiment analysis is enormously useful in social media supervising as it permits us to expand an impression of the wider open estimation behind definite topics. Investigation of social media streams is typically limited to just essential sentiment analysis and count based metrics. This is of the same kind to just scratching the outside and missing out on those elevated value insight that is ahead of you to be discovered. There’s a lot of effort to be done, but perfections are being prepared every day. It is a way to appraise on paper or verbal language to settle on if the expression is favorable, unfavorable, or unbiased, and to what level. Today’s algorithm-based sentiment analysis tools can touch vast amount of client response constantly and precisely. Balancing with text analytics, sentiment analysis exposes the customer’s estimation concerning topics ranging from your goods and services to your position, your advertisements, or even your challengers. These efforts scrutinize the crisis of studying texts, like posts and reviews, uploaded by user on Twitter. The Support Vector Machine (SVM), k-nearest neighbors algorithm (KNN) and proposed optimized feature sets model is offered to progression the tweet features and to recognize the out of sight sentiments from these tweets. These essential concepts when used in combinations become a very significant tool for analyzing millions of variety conversations with human echelon accurateness. The projected optimized feature sets model Sentiment Analysis exercise the assessment metrics of Precision, Recall, F-score, and Accuracy. Also, average measures weighted F1-scores are constructive for categorization of Positive, Negative and Neutral multi-class problems. The running time of the technique is evaluates by accomplishing diverse methods in the same investigational setup consisting a cluster of 8 nodes. Planned optimized feature sets model Sentiment Analysis reachs 82 % accuracy as compare with SVM 78.6 % and KNN 75 %. Further, while analyzing sentiments of tweets we have measured only tweets in English acknowledged by Twitter streaming API.

An approach for bibliographic citation sentiment analysis using deep learning

2021 ◽  
Vol 24 (4) ◽  
pp. 353-362
Author(s):  
Satish Muppidi ◽  
Satya Keerthi Gorripati ◽  
B. Kishore
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Negative Polarity ◽  
Research Area ◽  
Significant Feature ◽  
Research Papers ◽  
Feature Sets ◽  
Bibliographic Citation ◽  
Science Group

Sentiment analysis of scientific citations is a novel and remarkable research area. Most of the work on opinion or sentiment analysis has been suggested on social platforms such as Blogs, Twitter, and Facebook. Nevertheless, when it comes to recognizing sentiments from scientific citation papers, investigators used to face difficulties due to the implied and unseen natures of sentiments or opinions. As the citation references are reflected implicitly positive in opinion, famous ranking and indexing prototypes frequently disregard the sentiment existence while citing. Hence, in the proposed framework the paper emphasizes the issue of classifying positive and negative polarity of reference sentiments in scientific research papers. First, the paper scraps the PDF articles from arxiv.org under the computer science group consisting of articles that are comprised of ‘autism’ in their title, then the paper extracted cited references and assigns polarity scores to each cited reference. The paper uses a supervised classifier with a combination of significant feature sets and compared the performance of the models. Experimental results show that a combined CNN-LSTM deep neural network model results in 85% of accuracy while traditional models result in less accuracy.

scholarly journals Automatic Sentiment Analysis Model Creation using Multi Kernel Improved Extreme Learning Machine

2020 ◽  
Vol 8 (6) ◽  
pp. 2727-2735
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Extreme Learning Machine ◽  
Opinion Mining ◽  
Support Vector ◽  
Analysis Model ◽  
Feature Sets ◽  
Research Activities ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Recent research activities related to opinion mining, sentiment analysis and emotion detection from natural language texts are all under the umbrella of affective computation. There is now a huge amount of textual information on social media (for example, forums, blogs, and social media) about consumers' ideas about buying products and service experiences. Sentiment analysis or opinion mining is part of an investigation that analyzes people's thoughts and feelings from written text available online. In this paper, this work present a comprehensive experiment to evaluate the effectiveness of psychological and linguistic features in emotion classification. In this scheme, we used five broad categories of LIWC (namely, psychological processes, linguistic processes, punctuation, spoken categories and personal concerns) as feature sets. Five types of LIWCs and their group combinations were considered in the experimental analysis. To understand the predictive performance of various aspects of the engineering scheme, five controlled learning algorithms (namely, Naïve Bayes, support vector machines, Extreme Learning Machine, Kernel Extreme Learning Machine, Multi Kernel Extreme Learning Machine) and proposed Multi Kernel Improved Extreme Learning Machine (MKIELM) are used. Experimental results show that the ensemble feature sets provides a higher predictive effect than the individual set..

scholarly journals An Intelligent High Performance Automatic Sentiment Analysis Model Creation using Deep Convolution Neural Network

2020 ◽  
Vol 9 (3) ◽  
pp. 1288-1295
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
High Performance ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Analysis Model ◽  
Feature Sets ◽  
Deep Convolution Neural Network ◽  
Predictive Efficiency

With the advancement of data and communications technology, social media platforms and small news blogs serve as significant sources of data. In a small blogging forum, people can share their opinions, complaints, feelings and behaviors about the topic, current problems, and products. Emotional examination is an significant examination area in natural language processing that intends to target the emotion of the source material. Twitter is a well-liked stage where people around the globe can interrelate through user-produced messages. Data received from Twitter can give out as a primary source for many applications, together with event recognition, news recommendations as well as emergency supervision. In the categorization of emotions, recognition of suitable sub feature set acts an significant role. LIWC (Linguistic Inquiry and Word Count) is a research program for text examination to retrieve psychometric features from text documents. In this article this work present a psychometric method called the intelligent high performance automatic sentiment analysis model (IHPASAM) for Twitter emotion analysis. In this scheme, this work employed two main types of LIWC (linguistic processes along with psychological) as feature sets. To discover the predictive efficiency of dissimilar feature engineering systems, five supervised learning techniques (Naïve Bayes, logistic regression, k-nearest neighbor algorithm, support vector machines as well as convolution neural network) along with proposed Intelligent Deep Convolution Neural Network (IDCNN) are employed. Investigational outcome show that the ensemble feature sets provides a superior predictive efficiency than the individual set.

scholarly journals Sarcasm Detection For Sentiment Analysis in Indonesian Tweets

2019 ◽  
Vol 13 (1) ◽  
pp. 53 ◽  
Author(s):  
Yessi Yunitasari ◽  
Aina Musdholifah ◽  
Anny Kartika Sari
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Random Forest Algorithm ◽  
Feature Sets ◽  
Syntactic Features ◽  
The Social ◽  
Bayes Algorithm ◽  
The Moment

Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the  accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%.

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