Sentiment Analysis
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2021 ◽  
Vol 20 ◽  
pp. 149-167
Huanzhuo Ye ◽  
Yuan Li

This study proposes a service quality evaluation model framework which integrates automatic data acquisition, intelligent data processing and real-time data analysis with online comment data as data sources by introducing natural language processing technology based on management methods to break the traditional idea of over-reliance on human resources for service quality evaluation. The framework is mainly divided into text data preparation, fine-grained sentiment analysis and fuzzy cloud evaluation models. Data preparation module is responsible for preparing the initial data, and the fine-grained sentiment analysis module is responsible for pre-training a fine-grained sentiment classification model. The fuzzy cloud evaluation module uses the data obtained from the first two modules to evaluate service quality. By applying the model into catering industry, the feasibility of the model is proved and individuality, efficiency, dynamicity and intelligence of the model give it more advantage in the practice of service quality evaluation

2021 ◽  
pp. 097325862110311
N. Nurlaela Arief ◽  
Aria Bayu Pangestu

This study aims to evaluate the empathic brand initiative during the COVID-19 pandemic in Indonesia and analyse the online sentiments toward the philanthropy of corporations, non-profits organisations, citizens and society. Sentiment analysis was conducted on related posts of 15 companies from March to June 2020 with varying preliminary times for each company as the first donor. To complete the perception, the authors conducted a focus group discussion (FGD). Research shows that medium size and small–medium enterprises, such as local cosmetic companies and budget hotel are the first donors, followed by large or multinational companies (MNCs). In contrary with previous research, public perception was not influenced by the amount and the time of giving but was impacted by communication strategies, the empathy of the brand itself, and the company behaviour before COVID-19 period. This research’s novelty is the emphatic communication model to create, maintain and protect a company’s reputation.

2021 ◽  
Vol E104.D (8) ◽  
pp. 1274-1280
Weizhi LIAO ◽  
Yaheng MA ◽  
Yiling CAO ◽  
Guanglei YE ◽  
Dongzhou ZUO

Irina Wedel ◽  
Michael Palk ◽  
Stefan Voß

AbstractSocial media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.

Karsten Müller

AbstractBased on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 312
Alexandros Britzolakis ◽  
Haridimos Kondylakis ◽  
Nikolaos Papadakis

Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in-real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there.

2021 ◽  
Vol 48 (7) ◽  
pp. 790-801
Taehee Jeon ◽  
Changhwan Kim

Prof. Ranjanroop Walia

As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user. Recommender system (RS) has emerged as a major research interest that Aims to help users to find items online by providing suggestions that Closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval.

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254337
James Waters ◽  
Nicos Nicolaou ◽  
Dimosthenis Stefanidis ◽  
Hariton Efstathiades ◽  
George Pallis ◽  

Sentiment analysis is an evolving field of study that employs artificial intelligence techniques to identify the emotions and opinions expressed in a given text. Applying sentiment analysis to study the billions of messages that circulate in popular online social media platforms has raised numerous opportunities for exploring the emotional expressions of their users. In this paper we combine sentiment analysis with natural language processing and topic analysis techniques and conduct two different studies to examine whether engagement in entrepreneurship is associated with more positive emotions expressed on Twitter. In study 1, we investigate three samples with 6.717.308, 13.253.244, and 62.067.509 tweets respectively. We find that entrepreneurs express more positive emotions than non-entrepreneurs for most topics. We also find that social entrepreneurs express more positive emotions, and that serial entrepreneurs express less positive emotions than other entrepreneurs. In study 2, we use 21.491.962 tweets to explore 37.225 job-status changes by individuals who entered or quit entrepreneurship. We find that a job change to entrepreneurship is associated with a shift in the expression of emotions to more positive ones.

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