An Advance Approach toward Sentiment Analysis using Swarm Intelligence

Author(s):  
Miss. Riddhi Mandal

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.

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.


2019 ◽  
Vol 56 (6) ◽  
pp. 102097 ◽  
Author(s):  
Ziyuan Zhao ◽  
Huiying Zhu ◽  
Zehao Xue ◽  
Zhao Liu ◽  
Jing Tian ◽  
...  

2021 ◽  
Vol 11 (2) ◽  
pp. 8-15
Author(s):  
İbrahim Sabuncu ◽  
Berivan Edeş ◽  
Doruk Sıtkıbütün ◽  
İlayda Girgin ◽  
Kadir Zehir

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.


2020 ◽  
Vol 4 (2) ◽  
pp. 56-74
Author(s):  
Nadia Delanoy ◽  
arina Kasztelnik

This paper summarizes how social media and other technologies continue to proliferate; the shifting economic landscape will precipitate more adaptive approaches for managers attempting to understand the multi-dimensional virtual aspects of communication with the artificial intelligence aspect. Also, we discover the different existing support of big data analytics to make a rational business decision. The methodology is the systematization literature sources within this context and approaches for the underlining approach to open big data analytics and support innovative leadership decisions in Canada. The paper is carried out in the following logical sequence to gain an understanding of how customer relations managers could utilize social media within a data analytics frame from scholar and practitioner perspectives. This literature research review original paper outlines the main themes including the role of social media, the experiences of using data analytics for customer relations management, and the notion that customer-centric technologies could change the dynamic of understanding customer intentions, leadership decisions and introduce the innovative management with using the big data analytics in place. The results of the critical thinking with analysis both authors can be useful for any business around the World that would like to start using Artificial Intelligence to support innovative management decisions. The emergent themes that were highlighted based on the realities of customer relations management may be significant to how the integration of social media feedback resulting from crowdsourcing in addition to existing data analytics could better position organizations in this evolving world. The implications of linking innovative management processes such as demographic analysis, platform understanding, and communication methods together are crucial for any public business with a global impact. Finally, the understanding of innovation management in a social media era and understanding how customers utilized open big data analytics sources could help leadership practices across industries around the World. Keywords: Big Data Analytics, Innovative Leadership, Management of Social Media, Open Sources.


2019 ◽  
Vol 8 (2) ◽  
pp. 2421-2428

Social Media is a popular medium of communication amongst youngsters to remain connected with their friends. Facebook is one of the most preferred Social Media Sites which store the gigantic amount of data which can be explored for Sentiment Analysis. In this study, we have applied hybrid analysis approach which combines the best features of a lexical analysis and SVM machine learning classification algorithm on Facebook Posts. The analysis is further improved by incorporating language discourse features to detect intensity of sentiment and the prominent emotions expressed through these posts.


Author(s):  
Fatih Pinarbasi ◽  
Habib Mehmet Akpınar

Marketing lives as a living organism with its competitive environment and several internal conditions. Marketing functions should adapt to the world they are bound to and develop their activities by taking into account the changes and developments in micro and macro factors. With the increase in technology and the spread of social media, transformation in marketing has become inevitable. Advances in business applications have better results about understanding customer and markets. As previous studies examine artificial intelligence concept with different contexts, there is lack of integrated study which examine marketing and artificial intelligence together. This study proposes three-steps plan for implementing AI with marketing while it includes nine scenarios from different marketing goals. Study also has suggestions for implementing AI methodologies into business processes.


Author(s):  
V. Subramaniyaswamy ◽  
R. Logesh ◽  
M. Abejith ◽  
Sunil Umasankar ◽  
A. Umamakeswari

Social Media has become one of the major industries in the world. It has been noted that almost three fourth of the world's population use social media. This has instigated many researches towards social media. One such useful application is the sentimental analysis of real time social media data for security purposes. The insights that are generated can be used by law enforcement agencies and for intelligence purposes. There are many types of analyses that have been done for security purposes. Here, the authors propose a comprehensive software application which will meticulously scrape data from Twitter and analyse them using the lexicon based analysis to look for possible threats. They propose a methodology to obtain a quantitative result called criticality to assess the level of threat for a public event. The results can be used to understand people's opinions and comments with regard to specific events. The proposed system combines this lexicon based sentimental analysis along with deep data collection and segregates the emotions into different levels to analyse the threat for an event.


2020 ◽  
Vol 11 (1) ◽  
pp. 27-35
Author(s):  
Sandip Palit ◽  
Soumadip Ghosh

Data is the most valuable resource. We have a lot of unstructured data generated by the social media giants Twitter, Facebook, and Google. Unfortunately, analytics on unstructured data cannot be performed. As the availability of the internet became easier, people started using social media platforms as the primary medium for sharing their opinions. Every day, millions of opinions from different parts of the world are posted on Twitter. The primary goal of Twitter is to let people share their opinion with a big audience. So, if the authors can effectively analyse the tweets, valuable information can be gained. Storing these opinions in a structured manner and then using that to analyse people's reactions and perceptions about buying a product or a service is a very vital step for any corporate firm. Sentiment analysis aims to analyse and discover the sentiments behind opinions of various people on different subjects like commercial products, politics, and daily societal issues. This research has developed a model to determine the polarity of a keyword in real time.


This paper presents sentiment analysis of twitter data on movies using R-studio. Twitter is one of the largest social media that shares user opinion about a thing or event that happens all around the world. Recently social media analysis gained importance in digital marketing. User tweets about a product or event, person, movie, etc., are analyzed to know market trends and customer feedback. In this paper, first we have performed literature study on various methods used in twitter data analysis. Second, we have discussed about the steps involved in accessing twitter data. Finally, we have performed sentiment analysis on tweeter data for the movies titled kabali, Bharath Ane Nenu Mersal, and Dangal. User data for the movies are classified into positive, neutral and negative based on DBM and SVM. Sentiment scores are used as evaluation metrics. Results shows DBM is effective in classifying sentiments and produced better sentiment scores compared to SVM. Results are helpful in identifying popularity of the movies and audience feedback about the movies.


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