READING BETWEEN THE LINES: A QUALITATIVE ANALYSIS OF ONLINE PRODUCT REVIEWS

Abstract [English]

Overview: The rise of e-commercehas created a more interactive and competitive environment for both sellers and buyers. In recent times, a heavy reliance on online product reviews posted on the e-commerce website by existing and verified users has been witnessed. From the extant literature, it is evident that the impact of non–textual or quantitative aspects of online product reviews (OPR), such as volume, valence, star rating, helpful votes, etc., on buying behaviour, purchase intention, sales, etc. has been vastly studied. But since the past decade, the importance of analysing and understanding the textual data of OPR has been strongly recommended and explored.
Aim: Reviewers express their opinions through non-structured and unfiltered reviewswhere the information presented goes beyond mere words.The review text has embedded emotions and sentiments also. The current research aims to investigate and comprehend the implicit information conveyed by consumers in the unstructured and heartfelt reviews on Amazon.in. The study seeks to identify key semantic aspects affecting OPRs' assessment by highlighting latent topics, sentiments and intricacies within the textual content of the reviews.
Methodology: Amazon is the most popular e-commerce site in India, and its review system is widely seen as reliable, clear, and standardised. Its multi-dimensional format—textual reviews(review statements and title statements), aggregate star ratings which guarantee consistency across many product categories and offers a solid foundation forqualitative research. This study employed a multistage sampling technique to extract 5,900 reviews from 59 top-selling products across three best-selling categories: beauty, fashion, and electronics from Amazon India. Web scraping has been used to extract the review data using Python as a programming language. A qualitative content analysis, topic modelling, sentiment analysis and sentiment score analysis have been employed using various packages and functions in R Studio.
Findings: The research reveals that both negative and positive sentiments have significant effects on product ratings. The word count analysis indicated a predominant use of positive words such as ‘good’, ‘product’, ‘quality’, ‘nice’, and ' price’. The three distinct topics identified through topic modelling demonstrate that reviews are shaped by a combination of functional utility, sensory experience, and detailed product attributes. The sentiment analysis revealed that positive sentiment is more common than negative sentiment. Additionally, emotions like trust, anticipation and joy were predominant, while negative emotions such as disgust and anger were less frequently observed. A key finding is the asymmetrical effect, where negative sentiment has a notably stronger negative impact than positive sentiment.Although statistically significant, the relatively low R-squared value suggests that sentiment scores alone account for only a small part of the variance in product ratings, indicating that ratings are a complex outcome influenced by multiple factors beyond expressed sentiment.
Implications: The study highlights the multifaceted role of online product reviews in shaping consumer behaviour on e-commerce platforms. The predominance of positive emotions emphasises the value of building strong consumer confidence. At the same time, the asymmetrical effect of negative sentiment urges the need for businesses to address the dissatisfaction promptly. For practitioners, the findingssuggest that effective product management involves not only mitigating negative reviews but also actively leveraging positive consumer emotions and experiences to strengthen brand loyalty. Strategically, e-commerce businesses should adopt a dual approach—proactively managing negative feedback while amplifying positive narratives—to enhance customer satisfaction and trust. Academically, the research contributes by evidencing the interplay of emotional, semantic, and many other factors in review analysis, offering pathways for future studies to incorporate richer variables and advanced text mining techniques.