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

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 Challenges
with 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.

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

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