Microblog Sentiment Analysis Algorithm Research and Implementation Based on Classification

2015 ◽  
Author(s):  
Yanxia Yang ◽  
Fengli Zhou
Keyword(s):  
Sentiment Analysis ◽  
Analysis Algorithm

scholarly journals Sentiment Analysis of Movie Review using Machine Learning Techniques

2018 ◽  
Vol 7 (2.7) ◽  
pp. 676 ◽  
Author(s):  
V Uma Ramya ◽  
K Thirupathi Rao
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Naive Bayes ◽  
Multinomial Logistic Regression ◽  
Machine Learning Techniques ◽  
Analysis Algorithm ◽  
Learning Techniques ◽  
Almost All

Today's online world was fully filled up with blogs, views, comments, posts through various websites and social-surfs. People were habituated with posting every incident into blogs, messed with comments like text and emotions, which are a mixed bag of sad, happy, worry, cry etc. Analysing such data was called as Sentimental Analysis. To analysis, these unordered data we use new emerged technology algorithms. Machine learning a transpire technology which is engaged with almost all the fields, where its algorithms are more powerful that give with better faultless results. In this paper, we are analyzing tweets based on movie reviews using the Multinomial Logistic Regression, Naïve Bayes, and SVM algorithms to compare score value to show the best text analysis algorithm. 

scholarly journals Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1645
Author(s):  
Ishani Chatterjee ◽  
Mengchu Zhou ◽  
Abdullah Abusorrah ◽  
Khaled Sedraoui ◽  
Ahmed Alabdulwahab
Keyword(s):  
Anomaly Detection ◽  
Sentiment Analysis ◽  
Outlier Detection ◽  
State Of The Art ◽  
Correction Method ◽  
Machine Learning Techniques ◽  
True Nature ◽  
Analysis Algorithm ◽  
Star Rating ◽  
Customer Reviews

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

scholarly journals Fast Sentiment Analysis Algorithm Based on Double Model Fusion

2021 ◽  
Vol 36 (1) ◽  
pp. 175-188
Author(s):  
Zhixing Lin ◽  
Like Wang ◽  
Xiaoli Cui ◽  
Yongxiang Gu
Keyword(s):  
Sentiment Analysis ◽  
Model Fusion ◽  
Analysis Algorithm
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