Building a Sentiment Analytics Solution powered by Machine Learning- Impetus Webinar

Building a Sentiment Analytics Solution Powered by Machine Learning

May 11th, 201210:00 am PT/ 1:00 pm ET

@ impetustech

Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58

http://www.impetus.com/webinar_registration?event=archived&eid=58


Outline

Sentiment Analysis – Why? Solution landscape Addressing challenges with Machine Learning Building a sentiment analytics solution

Leveraging Machine learning and n-gram

Case Studies

2Impetus Proprietary




Sentiment Analysis

Determines the attitude of a speaker or a writer with respect to a particular subject, event or campaign

Computational study of opinions, sentiments, and emotions expressed in text





Measuring Sentiments





Natural Language Processing Deals with the actual text element Transforms text into a format usable by machine

Artificial intelligence Uses information by NLP and Mathematical calculations Determines negative, positive or neutral sentiments Used for clustering

Current Solutions Landscape





Sentiment Analysis: Challenges

Demystifying accuracy Inability of machines to gauge and measure sentiments

accurately

Isolating content types Neutral nature of social media mentions

Sentiment override User needs override control due to inaccuracy of automated

sentiment measurement





Machine Learning

Intelligent structure that acquires and integrates knowledge automatically

Learns from experience, training, analytical observation





Subjectivity vs Sentiment

Sentiment analysis - text classification problem

Segregate

Opinionated documents as per positive/ negative

Sentence or a clause of the sentence as subjective or objective

Subjective sentence or clause on the basis of positive, negative or neutral





Determining Polarity

Leveraging Machine Learning

Algorithm identifies positive/ negative/neutral sentiments

Refers knowledge bank to determine polarity of a new sentence or word

A knowledge bank is the database of pre-trained words and sentences classified as negative, neutral, positive





Predicting Sentiment Intensity

Benchmark neutral - say 40-50% of positive is neutral Intensity below the benchmark is negative and above is

positive.

Referring Knowledge Bank Continuously trained by a Machine Learning Algorithm Intensity predictions becomes accurate

Occurrence of a word or sequence of words in a particular polarity decides the intensity of the overall sentiment





How it works?





Text classification - n-gram

Ex: RUBBISH – NegativeRUBBISHING – NegativeRUBBISHED - Negative


Add Pattern to Dictionary

Building Knowledge Base





Leveraging Machine Learning





Impetus Solution

• Intuitively retrieves input text for analysis

• Processes various Low level APIs, REST APIs, enriched XML DOC, Text, or RSS

• Architecture enables exporting services in form of REST APIs

• Intuitive solution, capable of processing near real-time data using Big Data stack

• Concurrent processing system enables fast results with higher accuracy





What’s new?

Higher accuracy

Identifies influencers

Reputation as per demographics

Measuring sentiment intensity





Case Studies

Nokia Lumia (On Twitter)

Apple’s iPad3 (On Facebook)





Case study: Nokia

Search keyword - “Nokia Lumia” Total tweets analyzed - 9650 Accuracy - 99% Neutral Tweets - 42%

United States35%

United Kingdom

23%

Indonesia15%

Italy8%

France5%

India 5%

Germany3%

Mexico3%

Turkey2%

Canada1%

Demographics by –ve sentiment

7%9%

9%

10%

11%

11%

11%

11%

11%11%

Demographics by +ve sentiment

United States

United Kingdom

Indonesia

Italy

France

India

Germany

Mexico

Turkey

Canada

54%3%

42% 1%

Sentiments: Nokia Lumia

Positive Tweets

Negative Tweets

Neutral Tweets

False Positive


• Successful online campaign for Nokia

• Lumia• Pureview

• Nokia CRM @nokia (Official Channel) had positive mentions tweets

• Reputation very high for all Hashtags of #nokia, #lumia, #pureview

• Brand management measured via custom keywords search for nokia with no hash tags

- mentions with mostly positive sentiment

Case study: Nokia

Reputation Management

Brand Management

CRM


Search keyword “ipad3” Total FB status analyzed : 3200 Accuracy: 97% Neutral FB Status: 53%

Case study: iPad 3

31%

13%

53%3%

Sentiments: Apple’s iPad3

Positive Negative Neutral FP

48%

6%8%2

%

35%

Demographics: -ve Sentiment

United States Germany France Mexico Canada

23%

3%

4%

1%17%

52%

+ve Sentiment

United States Germany France MexicoCanada China


Overall Apple’s performance on online campaign was effective • Product: iPad3

Sentiment: Positive

• General brand perspective is positive

• CRM – Existing customers feel positive

Case study: iPad 3

Reputation Management

Brand Management

CRM





What It Looks Like?





Summing Up

Traditional Approach – NLP and Artificial Intelligence

We recommend - A Sentiment Analytics solution based on Machine Learning, n-gram and Bayes filter classification Addresses neutral social media mentions, sentiment override,

target overlook actual verbatim Ability to cross-reference intensity, influence trajectory, velocity and

sentiment of each social media mention





About Us

Strategic partners for software product engineering and R&D Thought leaders in cutting-edge technologies Mature processes and practices that are methodical, yet flexible Diverse domain expertise





Questions

Please send in your questions

using the chat panel





Thank youFor more information,

write to us at [email protected]

@ impetustech


mailto:[email protected]



Building a Sentiment Analytics Solution powered by Machine Learning- Impetus Webinar

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