Using Sentiment Analysis for Comparing Attitudes between Computer Professionals and Laypersons on the Topic of Artificial Intelligence

2019 ◽  
Author(s):  
Xueying Wang
Keyword(s):  
Artificial Intelligence ◽  
Sentiment Analysis ◽  
Computer Professionals

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

scholarly journals News Sentiment Analysis By Using Deep Learning Framework

2020 ◽  
Author(s):  
Sweta Kaman
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
News Article ◽  
Learning Mechanism ◽  
Attention Model ◽  
Learning Framework ◽  
News Sentiment ◽  
Document Level

Attention is a deep learning mechanism which has been proved very helpful in the field of artificial intelligence and solving various AI problems, in order to bend the various intelligent tasks positively in the direction to its actual goal i.e AI. In this paper, I have used Attention Model to perform the task of sentiment analysis in any news article. After extracting the news article from a scraper and preprocessing the data, it will be fed into a sentiment analyser which will predict the sentiment of the news article at sentence and document level.

scholarly journals Creating Brand Image Profile by Social Media Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 8-15
Author(s):  
İbrahim Sabuncu ◽  
Berivan Edeş ◽  
Doruk Sıtkıbütün ◽  
İlayda Girgin ◽  
Kadir Zehir
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Brand Image ◽  
Media Analysis ◽  
Alternative Methods ◽  
Social Media Analysis ◽  
Focus Group Study ◽  
Complementary Method ◽  
Mining Tool

The purpose of creating a brand image profile is to measure the brand perception of consumers considering brand attributes. Thus, marketing decisions can be made based on the brand's strengths and weaknesses by determining them. The brand image profile is traditionally created using the attitude scales and surveys. However, alternative methods are needed since the questionnaires' responses are careless, the number of participants is relatively low and the cost per participant is high. In this study, as an alternative method, creating a brand image profile by analyzing social media data with artificial intelligence was made for the iPhone product. Firstly, the focus group study determined the attributes related to the last version of the iPhone. Then, between December 17th, 2019 and March 23rd, 2020, 87.227 tweets that include these attributes in English were collected from the Twitter social media platform through the RapidMiner data mining tool. Sentiment analysis was performed on collected tweets by the MeaningCloud text mining tool. In this analysis, positive and negative emotions were tried to be detected through artificial intelligence algorithms. Net Brand Reputation Score (NBR) was calculated using the positive and negative tweets amount for each attribute separately. Brand image profile was created by skew analysis using NBR values. As a result, it is thought that social media analysis can be a complementary method that can be used with traditional methods in creating a brand image profile. So, it is seen as an inevitable method to use in further studies to make sentiment analysis by processing raw data received from the Social Media platforms through artificial intelligence algorithms to transform the product label or the perspectives of an event into meaningful information.

An Advance Approach toward Sentiment Analysis using Swarm Intelligence

2021 ◽  
Vol 9 (VI) ◽  
pp. 1710-1713
Author(s):  
Miss. Riddhi Mandal
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Swarm Intelligence ◽  
Crucial Role ◽  
Complex Problem ◽  
Analysis Approach ◽  
Customer Feedback ◽  
The World

Modernization is the key feature for the development of Society. With the timespan people are making growth with trends in technology. Around the decades, there were many technologies which have been stepped up over the industry and made the transformation in the society and have made tremendous development throughout the world. Similarly, In the 21st decades Social media (like Facebook, Twitter, what’s app, Instagram & many more) have become one of the emphasized network mediums. Millions of people are using social media to get in touch with people staying far away from them. There are millions of data over it which is non-hierarchical and need to store and use it for feedback and other usage. Not only in Social Media, in the business & marketing sector too, customer feedback plays a crucial role. For maintaining and segregating data in a systematic way, sentiment analysis is being used which makes the task easier and helps to understand the data in a better way. In this paper, we are presenting a sentiment analysis approach using Swarm Intelligence, which could be more beneficial in such tasks to solve the complex problem. The concept is correlated with technology Artificial Intelligence.

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