scholarly journals Analytical Marketing with Collective Perception

2017 ◽  
Vol 1 (1) ◽  
pp. 389-402
Author(s):  
Giuseppe D’Aniello ◽  
Massimo De Falco ◽  
Matteo Gaeta ◽  
Francesca Loia
Keyword(s):  
Social Networks ◽  
Fuzzy Logic ◽  
Sentiment Analysis ◽  
Specific Product ◽  
Analysis Techniques ◽  
Web Community ◽  
Quantitative Manner ◽  
Or Organization ◽  
Source Of Information ◽  
The Web

Social networks, forums and blogs are widely considered as a valuable source of information for many applications and in different domains. Being able to extract, analyze and use the knowledge, opinions and sentiments the users share on the Web can become a competitive advantage for any company or organization. Specifically, information about the feelings and the opinions of the users of a Web community with respect to a product or a service can be useful for marketing.  In this context, the concept of collective perception is gaining momentum as a way to process, evaluate and quantify the perception and the sentiment that a community of users share about a given phenomenon. In this work, we propose an approach, based on Fuzzy Logic and Sentiment Analysis techniques, which allows to evaluate, also in a quantitative manner, the collective perception of a Web community with respect to a specific product or service. Keywords: Collective Perception; Analytical Marketing; Fuzzy Logic; Sentiment Analysis.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2018 ◽  
Vol 9 (2) ◽  
pp. 111-120
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

scholarly journals Sentiment Analyses for Kurdish Social Network Texts using Naive Bayes Classifier

2015 ◽  
Vol 1 (4) ◽  
pp. 393
Author(s):  
Salam Abdulla ◽  
Mzhda Hiwa Hama
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Specific Product ◽  
Product Service

Language is a great tool to communicate and carry information. Moreover, it is used to express feeling and sentiment. These days sentiment analysis is one the most active field of research, to discover people's opinion about specific product, service or topic. The task of sentiment classification is to categories reviews of users as positive or negative from textual information of Social Networks like Facebook, Google+, Twitter and Blogs to determine the feeling of majority about specific topics. Kurdish language suffer from the unique and standard writing rules, grammar syntax and alphabet. Therefore, Kurdish people write their feeling in social networks in different ways. Some of them prefer to use the Arabic script style while others prefer to use Latin letters to express their feeling, further some people use their different accents and syntax and even sometimes they use English letters write their emotion. Therefore, for the purpose of  analytics for Kurdish sentiment analyses its proposed to use data mining classification techniques such as Naive Bayes classifier because of its strong independence assumption. In Experimental results, the Social Network comments are classified into positive or negative polarities. The accuracy of sentiment analysis is obtained 66% by using Naive Bayes classifier for unigram feature on Kurdish text dataset.

An Overview of Methodologies and Challenges in Sentiment Analysis on Social Networks

2020 ◽  
pp. 204-213
Author(s):  
Aditya Suresh Salunkhe ◽  
Pallavi Vijay Chavan
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Web Services ◽  
Sentiment Analysis ◽  
Real Time ◽  
Quality Analysis ◽  
Election Campaign ◽  
Real Time Analysis ◽  
User Comments ◽  
The Web

The expeditious increase in the adoption of social media over the last decade, determining and analyzing the attitude and opinion of masses related to a particular entity, has gained quite an importance. With the landing of the Web 2.0, many internet products like blogs, community chatrooms, forums, microblog are serving as a platform for people to express themselves. Such opinion is found in the form of messages, user-comments, news articles, personal blogs, tweets, surveys, status updates, etc. With sentiment analysis, it is possible to eliminate the need to manually going through each and every user comment by focusing on the contextual polarity of the text. Analyzing the sentiments could serve a number of applications like advertisements, recommendations, quality analysis, monetization provided on the web services, real-time analysis of data, analyzing notions related to candidates during election campaign, etc.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2020 ◽  
pp. 310-320
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

Sentiment Recognition from Bangla Text

2013 ◽  
pp. 315-327 ◽  
Author(s):  
K. M. Azharul Hasan ◽  
Sajidul Islam ◽  
G. M. Mashrur-E-Elahi ◽  
Mohammad Navid Izhar
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Real Life ◽  
Research Problem ◽  
Product Reviews ◽  
Important Research ◽  
Sentiment Orientation ◽  
Negative Sentiment ◽  
Source Of Information ◽  
The Web

Sentiment analysis is a very important area of the natural language processing. In general, sentiment classification means the analysis to determine the expression of a speaker whether he or she holds positive or negative opinion to a specific subject. With the rapid growth of e-commerce, sentiment analysis can greatly influence everyone in their real life. For example, product reviews on the Web have become an important source of information for customers’ decision making when they want to buy any product. As the reviews are often too many for customers to go through, how to automatically classify and detect the sentiment from them has become an important research problem. In this chapter, the authors present a Sentiment Analyzer that recognizes the Bangla sentiment or opinion about a subject from Bangla text. They construct some phrase patterns and calculate their sentiment orientation. They add tags to words in the Bangla text to construct the phrase pattern for positive and negative sentiment. Then the authors match the phrase pattern in Bangla text with their predefined phrase pattern and cumulate the sentiment orientation of each sentence.

scholarly journals Multi-Level Fine-Scaled Sentiment Sensing with Ambivalence Handling

2020 ◽  
Vol 28 (04) ◽  
pp. 683-697
Author(s):  
Zhaoxia Wang ◽  
Seng-Beng Ho ◽  
Erik Cambria
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Binary Classification ◽  
Analysis Techniques ◽  
Internet Users ◽  
Analysis Scheme ◽  
Different Types ◽  
Multi Level ◽  
Source Of Information ◽  
Analyze Data

Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and are not able to generate finer characterizations of sentiments as well as emotions involved. This paper describes a new opinion analysis scheme, i.e., a multi-level fine-scaled sentiment sensing with ambivalence handling. The ambivalence handler is presented in detail along with the strength-level tune parameters for analyzing the strength and the fine-scale of both positive or negative sentiments. It is capable of drilling deeper into text in order to reveal multi-level fine-scaled sentiments as well as different types of emotions.

An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter

2021 ◽  
Vol 16 (4) ◽  
pp. 52-73
Author(s):  
kamel Ahsene Djaballah ◽  
Kamel Boukhalfa ◽  
Omar Boussaid ◽  
Yassine Ramdane
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Fuzzy Logic ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Data Mining Algorithms ◽  
Terrorist Groups ◽  
Radical Content ◽  
Mining Algorithms

Social networks are used by terrorist groups and people who support them to propagate their ideas, ideologies, or doctrines and share their views on terrorism. To analyze tweets related to terrorism, several studies have been proposed in the literature. Some works rely on data mining algorithms; others use lexicon-based or machine learning sentiment analysis. Some recent works adopt other methods that combine multi-techniques. This paper proposes an improved approach for sentiment analysis of radical content related to terrorist activity on Twitter. Unlike other solutions, the proposed approach focuses on using a dictionary of weighted terms, the Word2vec method, and trigrams, with a classification based on fuzzy logic. The authors have conducted experiments with 600 manually annotated tweets and 200,000 automatically collected tweets in English and Arabic to evaluate this approach. The experimental results revealed that the new technique provides between 75% to 78% of precision for radicality detection and 61% to 64% to detect radicality degrees.

Social CRM

2016 ◽  
pp. 134-159
Author(s):  
Neus Soler-Labajos ◽  
Ana Isabel Jiménez-Zarco
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Personal Information ◽  
Single Source ◽  
Social Crm ◽  
The Social ◽  
Branding Strategies ◽  
Source Of Information ◽  
The Web ◽  
Interactive User

The advent of the web 2.0 in general and the social networks in particular has altered the consumer behavior with brands, consumer becoming the protagonist of his relationship with the companies. The consumer is no longer passive, but someone who belongs to an interactive user community, whose opinion influences the decision making of others and the company. And companies, therefore, need to understand how to structure content and branding strategies where clients not only communicate with the company, but each other, in real time: the social media. Social networks, as currently represent the opportunity to get the engagement of customers and prospects in a way that can not be achieved by other means, become a single source of information that must be integrated into the software of the company, allowing the conversion of conversation into a transaction. So having a database of clients with personal information is no longer enough, and is required to obtain qualitative features that enable the company to know more about consumers and provide, as a result, a greater brand value. The Social CRM is a tool that incorporates the information obtained from the social networks to the traditional CRM, to ensure that the company is better informed about its customers and gets a much more solid basis for decision making, customizing the offer and adding value to the customer.

Joint Embedding of Words and Labels for Sentiment Classification

2021 ◽  
Vol 31 (01) ◽  
Author(s):  
Yingwei Sheng ◽  
Inui Takashi
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Research Topic ◽  
Sentiment Classification ◽  
Word Embeddings ◽  
Baseline Model ◽  
Traditional Methods ◽  
Joint Embedding ◽  
The Web

With the fast growth of social networks, sentiment analysis on the web has been a popular research topic. Recently, word embedding-based sentiment analysis methods have reached outstanding performance compared to the traditional methods. However, word embeddings always ignore information from dataset’s labels. Inspired by the LEAM model proposed by Wang et al. [Joint embedding of words and labels for text classification (2018), arXiv:1805.04174], we propose a method that jointly learns information of words and sentiment labels, which can improve the performance of the label embedding model. We defined a set of sentiment lexicons and used it to represent sentiment labels in the proposed method. We finally conducted experiments on the Yelp dataset, which reached 65.03% accuracy when using the same setup as the baseline model, and 65.22% accuracy when using optional window sizes.

Sign in / Sign up

Export Citation Format

Share Document