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Directions towards a cool consumer review platform using machine learning (ml) and natural language processing (nlp)

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Consumer Reviews Ecosystem : Investigation and analysis of user needs, business requirements & technical approaches

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Directions towards a cool consumer review platform using machine learning (ml) and natural language processing (nlp)

  1. 1. copyright 2013 @ Dhwaj Raj 1Investigation andanalysis of user needs,business requirements& technical approachesConsumer Reviews EcosystemDhwaj Raj
  2. 2. copyright 2013 @ Dhwaj Raj 2What is a review ?
  3. 3. copyright 2013 @ Dhwaj Raj 3A set of users write reviews about someproducts or services and may assign ratings.Other or same set of users read reviews aboutsome products or services for the informationalintent or to make a purchase choice.
  4. 4. copyright 2013 @ Dhwaj Raj 4Is that it? All we need a posting formand a db reader?Naah! world isnt that simple.
  5. 5. copyright 2013 @ Dhwaj Raj 5How to build a technical solution?
  6. 6. copyright 2013 @ Dhwaj Raj 6●Who is the client?web users / consumers / brands / merchants●Need to understand user expectation and behavior●Do we know the requirements? We can think we knowbut not unless we know the market●Technology? Yes we will make informed decisionsabout the core engine but user experience plays acrucial role here.
  7. 7. copyright 2013 @ Dhwaj Raj 7Little push : Where to start from ?
  8. 8. copyright 2013 @ Dhwaj Raj 8 Examine the role and impact of reviews in the alreadyexisting review systems. Identify factors which influence review readers evaluationsof a review Investigate the influence of consumer generated reviews Identify motivations and barriers to posting reviews
  9. 9. copyright 2013 @ Dhwaj Raj 9We did some investigation onsample product reviews.....
  10. 10. copyright 2013 @ Dhwaj Raj 101.consumer reviews reflect quality rather than utility(value of quality for less price).2.When price is not fixed over time or acrosscompetetions then price has a direct influence onratings.3.There is difference between consumers who postreviews and those who do not.4.There is difference between frequent online reviewreaders and occasional readers.5.Late adopters/users having an "expectation" for aproduct based on prior reviews and their rating is thenimpacted based on whether or not the product metexpectations.
  11. 11. copyright 2013 @ Dhwaj Raj 11What we analyzed from sampleproduct reviews?
  12. 12. copyright 2013 @ Dhwaj Raj 121. The review of a product must be rated severaltimes by different users.2. Review should be according to several aspects,features or functionalities of the product.3. Several reviews are not rated. We can use oursystem to learn from the rated reviews to rate theothers.
  13. 13. copyright 2013 @ Dhwaj Raj 13Targeting users is cool!But can we add value to the brandsor products ?
  14. 14. copyright 2013 @ Dhwaj Raj 141. Provide an insight report for the structure of product ratingsover time.2. Provide stats to help them altering their marketingstrategies.3. Use prediction models to design pricing, advertising, orproduct design based on the sentiment trend across timeline.4. We can use spotlights and ranking based presentations toconvert the limited number of vocal buyers to the advocatesof the product.
  15. 15. copyright 2013 @ Dhwaj Raj 155. Brands can pay to encourage consumers likely to yieldpositive reports to self-select into the market early andgenerate positive word of mouth for new products.6. Provide an insight report on the weight that customersplace on each individual product feature.7. Provide the implicit evaluation score/rating that customersassign to each feature.8. Predict how these evaluations affect the revenue for agiven product.
  16. 16. copyright 2013 @ Dhwaj Raj 16Important Observation!We need Reader Comments aboutReviews
  17. 17. copyright 2013 @ Dhwaj Raj 17Reviews only tell the experiences and evaluations ofreviewers about the reviewed products or services.Comments, on the other hand, are readers evaluations ofreviews, their questions and concerns.The information in comments is valuable for both futurereaders and brands.Reader comments help the machine learning system tocorrelate product attributes, topics etc being discussed.
  18. 18. copyright 2013 @ Dhwaj Raj 18Heuristics NLP"great review", "review helped me" in Thumbs-up;"poor review", "very unfair review" in Thumbs-down;"how do I", "help me decide" in Question;"good reply", "thank you for clarifying" in Answer Acknowledgement;"I disagree", "I refute" in Disagreement;"I agree", "true in fact" in Agreement.Max-Ent priors for NLP can also detect"level headed review", "review convinced me" in Thumbs-up;"biased review", "is flawed" in Thumbs-down;"any clues", "I was wondering how" in Question;"clears my", "valid answer" in Answer-acknowledgement;"I dont buy your", "sheer nonsense" in Disagreement;"agree completely", "well said" in Agreement
  19. 19. copyright 2013 @ Dhwaj Raj 19Need of the hour :User Satisfaction
  20. 20. copyright 2013 @ Dhwaj Raj 20The reviewers, serve as the driving force.Aim should be to keep the reviewers satisfied andmotivated to continue submitting high-qualitycontent is essential.Help potential buyers by focusing on high-qualityand informative reviews.
  21. 21. copyright 2013 @ Dhwaj Raj 21What demotivates a user?dont know why, havent thought about posting,dont shop enough, forget, Internet access problems,plan on starting …..........
  22. 22. copyright 2013 @ Dhwaj Raj 221. Ugly text field forms.2. Time constraints.3. Lack of confidence in writing.4. Being Lazy.etc. etc....
  23. 23. copyright 2013 @ Dhwaj Raj 23How to keep user motivated?
  24. 24. copyright 2013 @ Dhwaj Raj 241. Utilization of expertise: Predict if a person may beperfectly capable to comment on more attributes than heintends to.2. User Expereince Design: Use question asking model todrive the user intent.3. Capitalize on the users genuine desire to help others.4. Allow the expression of frustration or excitement due tothe reviewed item, the desire to influence others.5. Use gamification of credits like quora.
  25. 25. copyright 2013 @ Dhwaj Raj 256. System should give acknowledgment for positive ratings.7. To deal with the information overload present them with asmall comprehensive set of reviews that satisfies theirinformation need using the Summarization.8. Use collaborative filtering to undertsand user choices.9. Predict readers intent : System should guarantee that usersare presented with a compact set of high-quality reviews thatcover all the attributes of the item of their interest.
  26. 26. copyright 2013 @ Dhwaj Raj 2610. We will present a mechanism for suggesting to reviewers how toextend their reviews in order to gain more visibility.11. Suggest attributes he can add or text spelling/language he maychange to achieve high quality score.12. Give them a quality rating or search rating and suggestions.13. Each eligible review needs to have a fair chance of inclusion in thespotlight/timeline sequence, according to the information it conveysand not just the filtering criteria.14. Use generic formalism to prevent overload : top few high-qualityreviews may be highly redundant, repeating the same information, orpresenting the same positive (or negative) perspective.
  27. 27. copyright 2013 @ Dhwaj Raj 27What do you mean byQuality of a review?
  28. 28. copyright 2013 @ Dhwaj Raj 281. A high-quality review must provide complete and timely informationabout a product with large number of opinions.2. The content of a medium-quality review is relevant to a product,but it is not informative enough. They hardly persuade readers tomake decisions.3. A low-quality review contains little information about a product, orthe information is too objective to judge the value of the product.4. A review is considered a duplicate if its content is very similar to areview posted previously.5. A spam review only provides other brands and services or it may bean advertisement or a question-answer type of review.
  29. 29. copyright 2013 @ Dhwaj Raj 29Some technical stuff !What features will classify thequality of a review?
  30. 30. copyright 2013 @ Dhwaj Raj 301. Believability : The product rating deviation of a review etc.2. Objectivity : If an information item is biased. Use Sentimentanalysis to capture subjectivity and opinion sentences.3. Reputation : If the author of a review is trusted or highlyregarded.4. Relevancy statistics : Helpful product reviews should provide alarge amount of product information.5. Ease of Understanding : good language and clear opinions
  31. 31. copyright 2013 @ Dhwaj Raj 316. Timeliness : if the information in a review is timely andup-to-date.7. Completeness : if the information in a review is completeand covers various aspects of a product.8. Amount of Information : if volume of product informationin a review is sufficient for decision-making.9. Concise Representation : it complements the dimensionof the appropriate amount of information. Including a lotof information may result in a review that is too long.
  32. 32. copyright 2013 @ Dhwaj Raj 32Data ChallenegesYes we will tackle em !
  33. 33. copyright 2013 @ Dhwaj Raj 33measure the true quality of the product, merchant orservice?remove the bias of individual authors or sources?compare reviews obtained from different websites,where ratings may be on different scales (1-5 stars,A/B/C, etc.)?filter out unreliable reviews to use only the ones with"acceptable quality"?
  34. 34. copyright 2013 @ Dhwaj Raj 34Technical Challengeswith the given data scenario will behandled !
  35. 35. copyright 2013 @ Dhwaj Raj 35Filtering out spamLack of data? Aggregating data from other sourcesCalculating reviewer credibilityCalculate product ranking scoresIdentify sarcastic sentences to improve classificationIdentify sentences that do not relate to the product itself.
  36. 36. copyright 2013 @ Dhwaj Raj 36Did we miss something on UserExperience Design for such asystem ?
  37. 37. copyright 2013 @ Dhwaj Raj 37The system will be such that with a single glance of itsvisualization, the user will be able to clearly see thestrengths and weaknesses of each product.This comparison is useful to both potential customers andproduct manufacturers.For a product manufacturer the comparison enables it toeasily gather marketing intelligence and productbenchmarking information.Use language pattern mining to highlight product featuresfrom Pros and Cons in a particular type of reviews.
  38. 38. copyright 2013 @ Dhwaj Raj 38High Level ComponentsUser Experience DesignSummarizationSentiment AnalysisStatistical Feature ExtractorReview Quality AnalyzerBayesian Model based review sorting.Several other Predictors and Classifiers.SearchNavigation cum auto-suggestorClustering…...and many more …...
  39. 39. copyright 2013 @ Dhwaj Raj 39Last but not the least : Always focuson Self Branding and PreceptionIn a poll about quora, helping the company was reported as a largemotivation because good service providers should be supportedto be successful
  40. 40. copyright 2013 @ Dhwaj Raj 40Thank you.Thank you.

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