To get best of both worlds Gemsim LDA models are trained on Spark cluster in embarrassingly parallel fashion. Utilizing Spark cluster capabilities provides huge performance benefits, in terms of parallel training, and reducing overall training time. Technical bottleneck with Gensim is that it runs on single cluster machine and cannot be trained on partitioned. Gensim model produced much more comprehensive results compared to Spark’s LDA model. Upon training on a smaller sample of data. Additionally, we have Gensim’s LdaMallet models, which are very good at finding useful topics in text corpus. Spark MLlib comes packaged with LDA models. LDA models are very popular for the task of Topic modeling. Documents are lemmatized using Spacy “en_core_web_lg” dictionary.Documents are tokenized using Gensim’s “simple_preprocess”.Using Regular Expressions Email addresses, and URLs are removed.In out Analysis we have performed Topic Modeling on “review_body”.įollowing steps are taken to paperer the data: Model Development: Spark (Pandas UDF), Gensim (LDA Model)ĭata used is the study is publically available data provided by Amazon:.Numerical Packages: Numpy, Pandas, PySpark.Databricks Platform for developing ML model.The analysis helps in identifying customer pain points with the products. In following sections I will provide NLP Topic Analysis I have applied to research Amazon Customer Reviews data. Know what features of our product are working, what features need improvement and what are customer expectations from our product. We can develop insights on our product features. Natural language processing equips us with tools, which can help us read and understand customer reviews. Human emotion and language biases can also hampers in extraction of actionable insights from reviews.Humans may get caught up with impression from a single review, which may not be representative, or most resourceful to act on. Human readers have difficulty in organizing information in unbiased fashion.Reviews are voluminous, for popular products reviews can range form few thousands to millions, making it infeasible for a human reader to completely read them.However, reading reviews manually causes following problems: Additionally, customers often mention their expectation in reviews. Reading review texts is defiantly a better option to extract customer insights, you get to understand about what features are helping customers solve their problems and what features are causing problems to customers. Star-Ratings are useful to gauge at a high level how your product is being received in the market, but it does not provide any actionable insights into what you should do to improve your product offerings. However, to leverage customer’s input in your product development you need more than just “Star-Ratings”. You have direct access to your customers mind. If you sell your products on e-platforms: Amazon, Ebay, Appstore, Playstore, Youtube, etc. The blog is posted by WeCloudData’s Bid Data course student Udayan Maurya.Ĭustomer reviews are invaluable information to understand the gap in your product market fit.
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