scholarly journals Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media

2018 ◽  
Author(s):  
Filippo Chiarello ◽  
Andrea Bonaccorsi ◽  
Gualtiero Fantoni ◽  
Giacomo Ossola ◽  
Andrea Cimino ◽  
...  
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Decision Process ◽  
New Products ◽  
Point Of View ◽  
Content Sharing ◽  
Technological Complexity ◽  
Box Office ◽  
Future Outcomes

In recent years, social media have become ubiquitous and important for social networking and content sharing. Moreover, the content generated by these websites remains largely untapped. Some researchers proved that social media have been a valuable source to predict the future outcomes of some events such as box-office movie revenues or political elections. Social media are also used by companies to measure the sentiment of customers about their brand and products. This work proposes a new social media based model to measure how users perceive new products from a technical point of view. This model relies on the analysis of advantages and drawbacks of products, which are both important aspects evaluated by consumers during the buying decision process. This model is based on a lexicon developed in a related work (Chiarello et. al, 2017) to analyse patents and detect advantages and drawbacks connected to a certain technology. The results show that when a product has a certain technological complexity and fuels a more technical debate, advantages and drawbacks analysis is more efficient than sentiment analysis in producing technical-functional judgements.

scholarly journals La interactividad en medios sociales: un estudio de caso / Social Media Interactivity: a Case Study

2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Joan Francesc Fondevila Gascón ◽  
Ana Beriain
Keyword(s):  
Social Media ◽  
Social Network ◽  
Empirical Study ◽  
Social Networking ◽  
Point Of View ◽  
Redes Sociales ◽  
Psychological Point ◽  
Medios Sociales ◽  
Estudio De Caso

ABSTRACTThe social networking phenomenon starts generating various investigations, but so far none has raised the relationships among users of a social network from the behavioral and psychological point of view. To this end, we have conducted an empirical study based on simulated profiles in Facebook, relevant social network due to the amount of available users and for its IPO. From imaginary profiles, we analyze the types of other Facebook users that are added, which can inspire ecommerce strategies related to digital newspapers.RESUMENEl fenómeno de las redes sociales comienza a generar investigaciones diversas, pero de momento ninguna ha planteado las relaciones entre los usuarios de una red social desde el punto de vista conductual y psicológico. A tal efecto, hemos llevado a cabo un estudio empírico a partir de una simulación de perfiles en Facebook, red social de referencia por la cantidad de usuarios disponibles y por su salida a bolsa. A partir de perfiles imaginarios, analizamos la tipología de otros usuarios de Facebook que se le agregan, lo que puede inspirar estrategias de comercio electrónico vinculadas a los periódicos digitales.

Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis

2021 ◽  
pp. 180-199
Author(s):  
Ainhoa Serna ◽  
Jon Kepa Gerrikagoitia
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Predictive Analytics ◽  
Data Gathering ◽  
Point Of View ◽  
Training Data ◽  
Complete Analysis ◽  
Sustainable Transport ◽  
Transport Modes

In recent years, digital technology and research methods have developed natural language processing for better understanding consumers and what they share in social media. There are hardly any studies in transportation analysis with TripAdvisor, and moreover, there is not a complete analysis from the point of view of sentiment analysis. The aim of study is to investigate and discover the presence of sustainable transport modes underlying in non-categorized TripAdvisor texts, such as walking mobility in order to impact positively in public services and businesses. The methodology follows a quantitative and qualitative approach based on knowledge discovery techniques. Thus, data gathering, normalization, classification, polarity analysis, and labelling tasks have been carried out to obtain sentiment labelled training data set in the transport domain as a valuable contribution for predictive analytics. This research has allowed the authors to discover sustainable transport modes underlying the texts, focused on walking mobility but extensible to other means of transport and social media sources.

Network Text Analysis and Sentiment Analysis

2016 ◽  
pp. 137-153
Author(s):  
Veronica Ravaglia ◽  
Luca Zanazzi ◽  
Elvis Mazzoni
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Social Networking Sites ◽  
Word Of Mouth ◽  
Network Text Analysis ◽  
Online Conversations ◽  
Web Forums

Through Social Media, like social networking sites, wikis, web forums or blogs, people can debate and influence each other. Due to this reason, the analysis of online conversations has been recognized to be relevant to organizations. In the chapter we introduce two strategic tools to monitor and analyze online conversations, Sentiment Text Analysis (STA) and Network Text Analysis (NTA). Finally, we propose one empirical example in which these tools are integrated to analyze Word-of-Mouth regarding products and services in the Digital Marketplace.

scholarly journals Multi-class Emotion AI by reconstructing linguistic context of words

2018 ◽  
Vol 7 (2.20) ◽  
pp. 97 ◽  
Author(s):  
K Sripath Roy ◽  
Farhaan Ahmed Shaik ◽  
K Uday Kiran ◽  
M Naga Teja ◽  
Subhani Kurra
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Learning Strategies ◽  
Point Of View ◽  
Linguistic Context ◽  
Social Networking Websites ◽  
Rich Data ◽  
Technological World

In today’s technological world, Social networking websites like Twitter, Instagram, Facebook, Tumblr, etc. play a very significant role. Emotion AI is about dealing, recognizing and analyzing sentiments or opinions conveyed in a person’s text. In particular Emotion is most frequently called Sentiment analysis. It helps us to understand the people’s point of view. A vast amount of sentiment rich data is produced by Social networking websites in the form of posts, tweets, statuses, blogs etc. Some users post reviews of certain products in social media which influences customers to buy the product. Companies can use such review data analyze it and improve the product. Sentiment analysis of Twitter is troublesome correlated to other social networking websites because of the existence of a lot of short words, misspellings and slang words applying emotion analysis to such data is more challenging. We have classified the sentiment into 5 categories. Machine learning strategies are preferred mostly for analyzing emotion AI. We have used neural network model word2vec with TF-IDF approach to predict the sentiment of the tweet. 

scholarly journals Detection and Analysis of Stress using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 335-342
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Text Messages ◽  
Word Sense ◽  
Severe Problem ◽  
Sense Disambiguation

Every year tens of millions of people suffer from depression and few of them get proper treatment on time. So, it is crucial to detect human stress and relaxation automatically via social media on a timely basis. It is very important to detect and manage stress before it goes into a severe problem. A huge number of informal messages are posted every day in social networking sites, blogs and discussion forums. This paper describes an approach to detect the stress using the information from social media networking sites, like tweeter.This paper presents a method to detect expressions of stress and relaxation on tweeter dataset i.e. working on sentiment analysis to find emotions or feelings about daily life. Sentiment analysis works the automatic extraction of sentiment related information from text. Here using TensiStrengthframework for sentiment strength detection on social networking sites to extract sentiment strength from the informal English text. TensiStrength is a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation. This classifies both positive and negative emotions based on the strength scale from -5 to +5 indications of sentiments. Stressed sentences from the conversation are considered &categorised into stress and relax. TensiStrength is robust, it can be applied to a widevarietyofdifferent social web contexts. Theeffectiveness of TensiStrength depends on the nature of the tweets.In human being there is inborn capability to differentiate the multiple senses of an ambiguous word in a particular context, but machine executes only according to the instructions. The major drawback of machine translation is Word Sense Disambiguation. There is a fact that a single word can have multiple meanings or "senses." In the pre-processing partof-speech disambiguation is analysed and the drawback of WSD overcomes in the proposed method by unigram, bigram and trigram to give better result on ambiguous words. Here, SVM with Ngram gives better resultPrecision is65% and Recall is 67% .But, the main objective of this technique is to find the explicit and implicit amounts of stress and relaxation expressed in tweets. Keywords: Stress Detection, Data Mining, TensiStrength, word sense disambiguation.

scholarly journals A Crowdsourcing Based Framework for Sentiment Analysis

2020 ◽  
Vol 16 (4) ◽  
pp. 285-295
Author(s):  
Fatima Zohra Ennaji ◽  
Abdelaziz El Fazziki ◽  
Hasna El Alaoui El Abdallaoui ◽  
Hamada El Kabtane
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Real Life ◽  
Data Sources ◽  
Valuable Data ◽  
Analysis Process ◽  
Validity Check ◽  
Online Platforms ◽  
Majority Consensus

As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers’ tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers’ credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design.

scholarly journals Sentiment analysis of social media content in Croatian health industry

2021 ◽  
Vol 9 (1) ◽  
pp. 37-57
Author(s):  
Jelena Mušanović ◽  
Jelena Dorčić ◽  
Tea Baldigara
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Modern Society ◽  
Essential Elements ◽  
Point Of View ◽  
Daily Routine ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality ◽  
Facebook Pages

While social media have become a daily routine in modern society, brand communication and engagement with customers have become essential elements of marketing strategy and success in the tourism and hotel industry. This revolution of social media, in tourism and hospitality marketing, contributed to the rise of a novel sentiment analysis from a machine learning and natural language processing point of view. The purpose of the study is: to provide a general descriptive overview of comments posted by Facebook page followers; to identify specific textual attributes of hotel brand posts on social media and to apply the sentiment analysis to Facebook comments from four- and five-star hotel brands in Croatia to identify and compare customers’ feelings and attitudes towards the staff, services and products that hotel brands promote by posting messages on Facebook pages. To analyse hotel brand sentiments, the authors collected a total of 4,248 comments and 2,373 postings in English, German and Italian. The results showed that the comments on four- and five-star hotel brands expressed predominantly positive sentiments. Despite the positively oriented sentiments in the comments, Facebook page followers are predominantly passive users and do not tend to comment actively. The results can be used by marketers in the tourism and hospitality industry to plan their future social media communication strategies.

Computing Opinions for Twitter Review Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4360-4363
Author(s):  
S. Tenkale Pallavi ◽  
S. Jagannatha
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Web Pages ◽  
Human Being ◽  
Association Network ◽  
The Social ◽  
Individual Human

Customers and users post their opinions or reviews on social networking sites and it has increased the amount of data WWW. With this users from all over world try to share their opinions and sentiments on the blogging sites every day. Internet is being used in form of web pages, social media, and sometimes blogs which increases online portals sentiments, reviews, opinions, references, scores, and feedbacks are also generated by people. Twitter is the most famous micro-blogging site where users express their opinions in the form of tweets. The user can express their sentiments about various aspects e.g., books, celebrities, restaurants, various products, research, events, etc. All these opinions plays vital roles and they are quite important for various businesses, for government schemes, and for individual human being as well. Still, there are many curbs in mining reviews or opinions and process to calculate them. These limitations have turned into highland in investigating the actual gist of opinions and measuring its polarity. Hence, we recommend an inventive way to compute the sentiments for given reviews or opinions. This recommendation is centered on the social networking sites’ information of various Tweets, a word-emotion-association-network is put up in association to represent opinions and semantics that decides the base for the emotions (sentiment) analysis of opinion or reviews.

scholarly journals Ubiquitous Role of Social Networking in Driving M-Commerce: Evaluating the Use of Mobile Phones for Online Shopping and Payment in the Context of Trust

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402093953 ◽  
Author(s):  
Syed Far Abid Hossain ◽  
Zhao Xi ◽  
Mohammad Nurunnabi ◽  
Khalid Hussain
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Mobile Phones ◽  
Social Networking Sites ◽  
Online Shopping ◽  
Mobile Apps ◽  
Mobile App ◽  
Point Of View ◽  
Security Issues

The article analyzes the role of driving m-commerce with social networking and therefore provides insight into how the application of mobile apps influences customers’ perceptions on purchasing products online and on the mode of payment. The consumers are engaged in social interactions through the internet by the new opportunities provided by social media. These interactions provide and generate certain values for both businesses and consumers. An upsurge in the application of social media on mobile phones by users is evident, giving optimism and the ability to view the role of the integration of m-commerce into social media. Certain criteria like mobile app compatibility, trust, perceived value of mobile phone apps for online shopping, and online payment are examined from the point of view of consumers who purchase products, save purchase time, and provide easy use and security through social networking sites and m-commerce. Adoption of a digital mode of payment is affected by the education level of the consumers as, if they are internet savvy, they will be more inclined to use the digital payment mode. The article not only discusses the role of education in the better understanding of consumers toward the application of online modes of transaction through mobile phones, but also indicates that there are security issues, although these have been resolved to some extent by technological advances. Yet, there is need for the retailers as well as the consumers to achieve further technological progress.

scholarly journals Issues and Challenges on Social Networking Applications

2019 ◽  
Vol 8 (12) ◽  
pp. 3186-3189
Keyword(s):  
Social Media ◽  
Social Networking ◽  
User Generated Content ◽  
Community Based ◽  
Media Development ◽  
Internet Applications ◽  
Content Sharing ◽  
Online Communications ◽  
The Creation

Social media is the collective of online communications channels dedicated to community-based input, interaction, content-sharing and collaboration. Reduce the challenges of social networking applications has lifted the research and industrial attention towards the growth of social media development. Web and Mobile based internet applications that allow the creation, access & exchange of user generated content that is available on all the places. Examples of social media are Facebook, Twitter, Google+, Wikipedia, Linkedin, pinterest. In this paper discusses only in detail about the main issues and challenges in social media application, and also discuss about social networking applications.

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