Analyzing Users’ Sentiment Towards Popular Consumer Industries and Brands on Twitter

Abstract

Abstract-Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12,898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month. We find that users tend to be most positive towards manufacturing and most negative towards service industries. In addition, they tend to be more positive or negative when interacting with brands than generally on Twitter. We also find that sentiment towards brands within an industry varies greatly and we demonstrate this using two industries as use cases. In addition, we discover that there is no strong correlation between topic sentiments of different industries, demonstrating that topic sentiments are highly dependent on the context of the industry that they are mentioned in. We demonstrate the value of such an analysis in order to assess the impact of brands on social media. We hope that this initial study will prove valuable for both researchers and companies in understanding users' perception of industries, brands and associated topics and encourage more research in this field.

Sentiment Analysis- A tool for Data Mining in Big Data Analytics

“…When the airline industry analysis is broken down further, it is seen that the consumers are extremely negative towards Spirit, Delta, and United, whereas they are pretty positive towards Air-Mauritius, Sun-Country and Thomas Cook. This information is essentially crucial for these companies to know where they can reduce the service gap [6].…”

Airline Sentiment Visualization, Consumer Loyalty Measurement and Prediction using Twitter Data

“…There are images, links, emoticons and other forms of media included. Hence, the first step of our analysis is to clean the tweets we have collected [28]. Tokenization is also difficult due to the body of the text.…”

“…Tokenization is also difficult due to the body of the text. We would need to make sure that the @-mentions, emoticons, links and #hash-tags are preserved as individual tokens and not ignored, since these are equally important aspects of the analysis [27], [28]. In this research we follow the method as shown in Fig.…”

User-Generated Tweets about Global Green Brands: A Sentiment Analysis Approach

“…Sentiment analysis is described as the fi eld of study that analyzes people's opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities, such as products, services, organizations, individuals, issues, events, topics, and their attributes (Liu, 2012). In fact, rather than answering surveys about products and services, consumers freely express their thoughts and emotions on social media (Hu et al, 2017). Sentiment analysis tasks can be done at several levels, as suggested by Kumar and Sebastian (2012): word level, phrase or sentence level, document level, and feature level.…”

“…Indeed, sentiment monitoring might enable companies to improve product quality and services, assess the impact of promotional campaigns, drive sales, and identify new business opportunities. Another highly relevant outcome of measuring sentiment is gauging users' perceptions of companies (Hu et al, 2017;Jansen, Zhang, Sobel & Chowdury, 2009). Given their pressing environmental and sustainability-related concerns, companies have been carefully developing their identities as "green brands".…”