Post on 28-Nov-2014
description
Building a Sentiment Analytics Solution Powered by Machine Learning
May 11th, 201210:00 am PT/ 1:00 pm ET
@ impetustech
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Outline
Sentiment Analysis – Why? Solution landscape Addressing challenges with Machine Learning Building a sentiment analytics solution
Leveraging Machine learning and n-gram
Case Studies
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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
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Measuring Sentiments
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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
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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
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Machine Learning
Intelligent structure that acquires and integrates knowledge automatically
Learns from experience, training, analytical observation
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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
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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
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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
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How it works?
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Text classification - n-gram
Ex: RUBBISH – NegativeRUBBISHING – NegativeRUBBISHED - Negative
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Add Pattern to Dictionary
Building Knowledge Base
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Leveraging Machine Learning
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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
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What’s new?
Higher accuracy
Identifies influencers
Reputation as per demographics
Measuring sentiment intensity
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Case Studies
Nokia Lumia (On Twitter)
Apple’s iPad3 (On Facebook)
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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
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• 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
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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
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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
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What It Looks Like?
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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
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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
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Questions
Please send in your questions
using the chat panel
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Thank youFor more information,
write to us at inquiry@impetus.com
@ impetustech
Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58