The Web has changed the way that consumers express their opinions. They can now post
reviews of products and express their opinions on almost anything on t
he websites. potential customers often search online for product information and they often have
access to hundreds of product reviews from other customers. Some of the reviews are
found to be more helpful than other reviews as evidenced the potential customer’s
helpful ness vote. This online word
-of-
mouth (WOM) behavior represents new and
measurable sources of information.
Recent research has shown that helpfulness votes of customer reviews can have a positive
influence on sales. While it is clear that helpfulness vote
of a review is important, less is known about why certain pieces of online review are more helpful than others. Despite
the fact that, customers encounter a variety of emotions in a purchase situation and those
emotions are likely to be documented in the review, few researches have investigated
how emotions elicited by the review affect the helpfulness of the review
beyond the valence . Do discrete emotions have differential informational value in this case? Based on
cognitive appraisal theory, in the first essay of this dissertation, I examine how specific
emotions (hope, happiness, anxiety, disgust etc.) embedded in the review affect the
helpfulness votes of potential customer. I adopt a quantitative content analysis (Latent
Semantic Analysis) approach to m
easure emotions in these reviews.
live In the second essay of the di
ssertation,
I explore how the
topics of online reviews differ
between
positive and negative reviews.
E
xamination of real product reviews shows that
there are thematic differences
between
them
.
Also, service related complains are found to
be more helpful by potential customers.
This enables us to better understand the
conceptual differences in WOM
.
Lastly, in the third essay, I compare two text mining techniques: Latent Semantic
Analysis and P
robabilistic Latent Semantic Analysis (PLSA) in extracting common
themes in the positive and negativ
e product reviews. R
esults shows that the choice of text
mining approaches should be based on the goal of the marketing researcher. If the goal is
to learn
about a specific brand, PLSA might reveal more specific information. However,
if the goal is to learn about important aspects of a broader product category, LSA works
better in terms of interpretability.
The Web has changed the way that consumers express their opinions. They can now post reviews of products and express their opinions on almost anything on the websites. potential customers often search online for product information and they often have access to hundreds of product reviews from other customers. Some of the reviews are found to be more helpful than other reviews as evidenced the potential customer’s helpful ness vote. This online word-of-mouth (WOM) behavior represents new and measurable sources of information. Recent research has shown that helpfulness votes of customer reviews can have a positive influence on sales. While it is clear that helpfulness vote of a review is important, less is known about why certain pieces of online review are more helpful than others. Despite the fact that, customers encounter a variety of emotions in a purchase situation and those emotions are likely to be documented in the review, few researches have investigated how emotions elicited by the review affect the helpfulness of the reviewbeyond the valence . Do discrete emotions have differential informational value in this case? Based on cognitive appraisal theory, in the first essay of this dissertation, I examine how specific emotions (hope, happiness, anxiety, disgust etc.) embedded in the review affect the helpfulness votes of potential customer. I adopt a quantitative content analysis (Latent Semantic Analysis) approach to measure emotions in these reviews. live In the second essay of the dissertation, I explore how thetopics of online reviews differ betweenpositive and negative reviews. Examination of real product reviews shows that there are thematic differences between them. Also, service related complains are found to be more helpful by potential customers. This enables us to better understand the conceptual differences in WOM .Lastly, in the third essay, I compare two text mining techniques: Latent Semantic Analysis and Probabilistic Latent Semantic Analysis (PLSA) in extracting common themes in the positive and negative product reviews. Results shows that the choice of text mining approaches should be based on the goal of the marketing researcher. If the goal is to learn about a specific brand, PLSA might reveal more specific information. However, if the goal is to learn about important aspects of a broader product category, LSA works better in terms of interpretability.
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