Determining Customer Perceptions using Text Mining for an FMCG Product “Maggi” In India
Semila Fernandes1, Aarti Mehta Sharma2, Vidyasagar A3

1Semila Fernandes, Management Department, Symbiosis Institute of Business Management, Bengaluru, Symbiosis International (Deemed University), Bengaluru, India.
2Aarti Mehta Sharma, Management Department, Symbiosis Institute of Business Management, Bengaluru, Symbiosis International (Deemed University), Bengaluru, India.
3Vidyasagar A, Management Department, Symbiosis Institute of Business Management, Bengaluru, Symbiosis International (Deemed University), Bengaluru, India.

Manuscript received on 28 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 1437-1444 | Volume-8 Issue-11, September 2019. | Retrieval Number: J97800881019/2019©BEIESP | DOI: 10.35940/ijitee.J9780.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Purpose: With the popularity of social media, blogging, documents in the web, multiple text information is being generated every moment. Companies can gauge consumers’ sentiments by conducting analysis of tweets or Facebook posts and can take timely action to tweak promotional campaigns. In the beginning of 2015, Maggi noodles was banned by the then government. To track the sentiments of the people on the coming back of Maggi, the widely accepted micro blogging site, Twitter, is used. Twitter continuously generates different points of view on any given subject, relating to social issues, marketing issues etc. The challenge lies in understanding and analyzing these unstructured texts, figuring out the relevant information and transforming it into actionable cognizance. Methods: The paper extracts set of 500 Twitter posts containing “Maggi”. 500 tweets were extracted to avoid heavy computation. The data was extracted by creating an interface between a twitter account and the statistical software R where we used the graphical user interface RStudio. This paper analyses tweets on this popular packed food item “MAGGI” by using statistical software like R and Excel. The methodology performed sentiment analysis using text mining approach following steps of Data Extraction, Text Transformation, Analyze the data, Data representation and validation. Results: The paper conducts sentiment analysis on social media and examines consumer perception of “Maggi”. There are few negative tweets like “Tired of #Maggi”, “Hesitant to take the first bite of #Maggi #marketingmoves” but most of the tweets were in the favor of Maggi. Tweets like, “This new #Maggi ad will surely make you go nostalgic!!”, “3am with beloved curry maggi + boiled chicken”, “When you wanna eat healthy but you low on cash. #Yasssss #Maggi #Broccoli #Sausages #Protein” show strong positive sentiment. From the sentiment analysis conducted on Maggi noodles, there were more positive rather than negative responses towards Maggi’s reentry into the Indian market. Thus, the concept of sentiment analysis can give marketers quick, preliminary insights into the consumers psyche which can later be followed up by traditional market research techniques.
Keywords: Customer Perception; Maggi; Social Media; Sentiment analysis; Text mining; Twitter. Manuscript Details
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