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

copyright 2013 @ Dhwaj Raj 1

Investigation and analysis of user needs, business requirements & technical approaches

Consumer Reviews Ecosystem

Dhwaj Raj


What is a review ?


A set of users write reviews about some products or services and may assign ratings.

Other or same set of users read reviews about some products or services for the informational intent or to make a purchase choice.


Is that it? All we need a posting form and a db reader?

Naah! world isn't that simple.


How to build a technical solution?


● 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 know but not unless we know the market

● Technology? Yes we will make informed decisions about the core engine but user experience plays a crucial role here.


Little push : Where to start from ?


Examine the role and impact of reviews in the already existing review systems.

Identify factors which influence review readers' evaluations of a review

Investigate the influence of consumer generated reviews

Identify motivations and barriers to posting reviews


We did some investigation on sample product reviews.....


1.consumer reviews reflect quality rather than utility (value of quality for less price).

2.When price is not fixed over time or across competetions then price has a direct influence on ratings.

3.There is difference between consumers who post reviews and those who do not.

4.There is difference between frequent online review readers and occasional readers.

5.Late adopters/users having an "expectation" for a product based on prior reviews and their rating is then impacted based on whether or not the product met expectations.


What we analyzed from sample product reviews?


1. The review of a product must be rated several times 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 our system to learn from the rated reviews to rate the others.


Targeting users is cool!

But can we add value to the brands or products ?


1. Provide an insight report for the structure of product ratings over time.

2. Provide stats to help them altering their marketing strategies.

3. Use prediction models to design pricing, advertising, or product design based on the sentiment trend across timeline.

4. We can use spotlights and ranking based presentations to convert the limited number of vocal buyers to the advocates of the product.


5. Brands can pay to encourage consumers likely to yield positive reports to self-select into the market early and generate positive word of mouth for new products.

6. Provide an insight report on the weight that customers place on each individual product feature.

7. Provide the implicit evaluation score/rating that customers assign to each feature.

8. Predict how these evaluations affect the revenue for a given product.


Important Observation!

We need Reader Comments about Reviews


Reviews only tell the experiences and evaluations of reviewers about the reviewed products or services.

Comments, on the other hand, are readers' evaluations of reviews, their questions and concerns.

The information in comments is valuable for both future readers and brands.

Reader comments help the machine learning system to correlate product attributes, topics etc being discussed.


Heuristics 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 don't buy your", "sheer nonsense" in Disagreement;

"agree completely", "well said" in Agreement


Need of the hour :

User Satisfaction


The reviewers, serve as the driving force.

Aim should be to keep the reviewers satisfied and motivated to continue submitting high-quality content is essential.

Help potential buyers by focusing on high-quality and informative reviews.


What demotivates a user?

don't know why, haven't thought about posting, don't shop enough, forget, Internet access problems, plan on starting …..........


1. Ugly text field forms.

2. Time constraints.

3. Lack of confidence in writing.

4. Being Lazy.

etc. etc....


How to keep user motivated?


1. Utilization of expertise: Predict if a person may be perfectly capable to comment on more attributes than he intends to.

2. User Expereince Design: Use question asking model to drive the user intent.

3. Capitalize on the user's genuine desire to help others.

4. Allow the expression of frustration or excitement due to the reviewed item, the desire to influence others.

5. Use gamification of credits like quora.


6. System should give acknowledgment for positive ratings.

7. To deal with the information overload present them with a small comprehensive set of reviews that satisfies their information need using the Summarization.

8. Use collaborative filtering to undertsand user choices.

9. Predict reader's intent : System should guarantee that users are presented with a compact set of high-quality reviews that cover all the attributes of the item of their interest.


10. We will present a mechanism for suggesting to reviewers how to extend their reviews in order to gain more visibility.

11. Suggest attributes he can add or text spelling/language he may change 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 the spotlight/timeline sequence, according to the information it conveys and not just the filtering criteria.

14. Use generic formalism to prevent overload : top few high-quality reviews may be highly redundant, repeating the same information, or presenting the same positive (or negative) perspective.


What do you mean by Quality of a review?


1. A high-quality review must provide complete and timely information about 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 to make decisions.

3. A low-quality review contains little information about a product, or the 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 a review posted previously.

5. A spam review only provides other brands and services or it may be an advertisement or a question-answer type of review.


Some technical stuff !

What features will classify the quality of a review?


1. Believability : The product rating deviation of a review etc.

2. Objectivity : If an information item is biased. Use Sentiment analysis to capture subjectivity and opinion sentences.

3. Reputation : If the author of a review is trusted or highly regarded.

4. Relevancy statistics : Helpful product reviews should provide a large amount of product information.

5. Ease of Understanding : good language and clear opinions


6. Timeliness : if the information in a review is timely and up-to-date.

7. Completeness : if the information in a review is complete and covers various aspects of a product.

8. Amount of Information : if volume of product information in a review is sufficient for decision-making.

9. Concise Representation : it complements the dimension of the appropriate amount of information. Including a lot of information may result in a review that is too long.


Data Challeneges

Yes we will tackle 'em !


measure the true quality of the product, merchant or service?

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"?


Technical Challengeswith the given data scenario will be

handled !


Filtering out spam

Lack of data? Aggregating data from other sources

Calculating reviewer credibility

Calculate product ranking scores

Identify sarcastic sentences to improve classification

Identify sentences that do not relate to the product itself.


Did we miss something on User Experience Design for such a

system ?


The system will be such that with a single glance of its visualization, the user will be able to clearly see the strengths and weaknesses of each product.

This comparison is useful to both potential customers and product manufacturers.

For a product manufacturer the comparison enables it to easily gather marketing intelligence and product benchmarking information.

Use language pattern mining to highlight product features from Pros and Cons in a particular type of reviews.


High Level Components

User Experience Design

Summarization

Sentiment Analysis

Statistical Feature Extractor

Review Quality Analyzer

Bayesian Model based review sorting.

Several other Predictors and Classifiers.

Search

Navigation cum auto-suggestor

Clustering

…...and many more …...


Last but not the least : Always focus on Self Branding and Preception

In a poll about quora, helping the company was reported as a large motivation because good service providers should be supported to be successful


Thank you.Thank you.

Date post:	28-May-2015
Category:	Education
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Directions towards a cool consumer review platform using machine learning (ml) and natural language...

Education