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Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysis

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  • Shimi Naurin Ahmad

    (Morgan State University)

  • Michel Laroche

    (Concordia University)

Abstract

User-generated content (UGC) contains customer opinions which can be used to hear the voice of customers. This information can be useful in market surveillance, digital innovation, or brand improvisation. Automated text mining techniques are being used to understand these data. This study focuses on comparing two common text mining techniques namely: Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (pLSA) and evaluates the suitability of the methods in two differing marketing contexts: Reviews from a product category and from a single brand from Amazon. The objectives of review summarization are fundamentally different in these two scenarios. The first scenario can be considered as market surveillance where important aspects of the product category are to be monitored by a particular seller. The second scenario examines a single product, and it is used to monitor in-depth customer opinions of the product. The results support that depending on the objective, the suitability of the technique differs. Different techniques provide different levels of precision and understanding of the content. The power of machine learning methods, domain knowledge and Marketing objective need to come together to fully leverage the strength of this huge user-generated textual data for improving marketing performance.

Suggested Citation

  • Shimi Naurin Ahmad & Michel Laroche, 2023. "Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 662-676, December.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:4:d:10.1057_s41270-023-00218-6
    DOI: 10.1057/s41270-023-00218-6
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    References listed on IDEAS

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