Twitter sentiment analysis of app based online food delivery companies

Abstract

Purpose There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies. Design/methodology/approach Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform. Findings Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided. Research limitations/implications The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy. Originality/value Twitter analysis of food-based companies has been performed.

Online food delivery companies' performance and consumers expectations during Covid-19: An investigation using machine learning approach

“…The second set of studies focused on the continuous usage intention of OFD apps during the COVID-19 outbreak period in different countries, such as India ( Mehrolia et al, 2021 ), the USA ( Hong et al, 2021 , Hong et al, 2021 ), China ( Zhao and Bacao, 2020 ), Brazil ( Zanetta et al, 2021 ), Mexico ( Ramos, 2021 ), Vietnam ( Tran, 2021 ). These and several other studies have found that performance expectancy ( Mehrolia et al, 2021 ; Zanetta et al, 2021 ; Pal et al, 2021 ; Ramos, 2021 ), habit ( Zanetta et al, 2021 ; Rydell and Kucera, 2021 ), effort expectancy ( Kumar and Shah, 2021 ; Ramos, 2021 ; Pal et al, 2021 ), price saving orientation ( van Doorn, 2020 ; Ramos, 2021 ; Pal et al, 2021 ), perceived usefulness, and employee trust ( Gavilan et al, 2021 ; Chakraborty et al, 2022 ; Trivedi and Singh, 2021 ; Uzir et al, 2021 ) affect consumers intention to use OFD services during the COVID-19 period.…”

“…Several studies ( Dsouza and Sharma, 2021 ; Mehrolia et al, 2021 ; Pal et al, 2021 ) have been conducted to understand OFD companies and customers' purchase behaviors in the Indian market. Trivedi and Singh (2021) found that Zomato received the most positive and less negative customer sentiments than other competitors, such as Swiggy in India. Dsouza and Sharma (2021) found that the food quality and safety measure of OFDAs positively affects customer satisfaction and loyalty in India.…”

“…People have used several ways to deal with the stress, anxiety, and loneliness caused by the COVID-19 (Kumarand Shah, 2020; Ahorsu et al, 2020 ; Lo Coco et al, 2021 ). Consumers have extensively utilized different social media platforms to share their opinion and remain updated with the public policies and regulations during the COVID-19 ( Rydell and Kucera, 2021 ; Trivedi and Singh, 2021 ). Several researchers studied how the COVID-19 has changed consumers' behavior, habits, and tastes when buying food online ( Watson and Popescu, 2021 ; Birtus and Lăzăroiu, 2021 ; Rydell and Suler, 2021 ; Smith and Machova, 2021 ).…”

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

“…Over the past couple of decades, there has been a good amount of research published on the application of ML techniques to perform sentiment analysis in the FDS domain [ 13 , 28 , 29 , 30 , 31 ]. Sentiment analysis of customer reviews from tweets for various FDSs, such as Swiggy, Zomato and Uber Eats, was performed to understand consumer satisfaction [ 32 ]. The customer reviews were pulled from Twitter using R-Studio, and the Lexicon-based sentiment analysis method was used on the tweets.…”

How India's food-tech companies are mining customer emotions from tweets

“…A research paper by Trivedi and Singh (2021) analyzes the social media content of customers, as a way of gathering data with which to understand their sentiment towards a product or service. In this case, data were gathered from the Twitter accounts of the three main online food app delivery services operating in India – namely the food-tech companies UberEats, Zomato, and Swiggy.…”