“Hybrid Sentiment Analysis Utilizing Multiple Indicators To Determine

Temporal Shifts of Opinion in OSNs”

April 19th, 2016

Joshua White, Robert Hall, Jeremy Fields, Holly White

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Introduction Shifts in Opinions Dataset

– Dataset Storage Schema Analysis

– Language Characteristics

– Demographic Characteristics• Gender

• Location

• Group Affiliation

Conclusion / Future Work References / Contact Info

Overview

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Social networks allow individuals to share ideals with like minded people at a faster/broader rate than ever before.

– This is true for “extreme” ideals as well (Danger) We continue to attempt to understand the mechanisms of change

in opinion– Both public opinion and individuals (over time, not suddenly)

Two Major Findings:

– We find that groups are affected most by high confidence level “experts”, typically males, who imbue trust

• Equally, Undecided or uninformed individuals have a positive affect on these groups . (Increasing group rationality)

– We find clusters of low confidence, like minded individuals, increase overall confidence in a group through positive feedback mechanisms

• Women are more likely to comprise the other two groups [1]

Introduction

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Shifts of public opinion has been the object of research for some time (psychology / sociology)

– Doing so at scale is fairly new

– Most progress in the area has resulted from increased computation capabilities

• The ability to simulate or replay long term changes– Actual lab investigations at this level would be impractical

– Researchers have identified three primary actors in change of sentiment (As discussed previously):

• The expert

• The undecided/uninformed

• Clusters of low confidence individuals

Shifts in Opinions

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Experts are actors with a high level of certainty (confidence)

– Doesn't need to actually be an expert

– If the percentage of experts within a group hits ~15% then they can affect group opinion

– Often the only offer vague amounts of actual knowledge Shifts of individuals who are (uninformed or undecided),

not due to expert influence are considered to be noise Clusters of low confidence individuals with congruent

opinions great stable state (majority rule)– This also creates a positive “boost” feedback in their own

confidence.

Shifts in Opinions

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Trust– In the case of this work was found to be important when

compounded with distance of similarity

– Higher trust = higher shifts in opinion• Especially if the trust was for an “Expert”

– Actors with similar interests were found to increase confidence in a bidirectional manner

– Actors with high dissimilarity between ideas were found to have negligible effects on each others opinions [2]

– Example:• Democrats and Independents who trusted scientist became

increasingly concerned with global warming where as increased knowledge was uncorrelated to concern in skeptics of scientists and among Republicans [3]

Shifts in Opinions

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Started with a series of political hashtags that were collected as part of a previous research project, researchers at SUNY Polytechnic collected 9Million+ tweets from the trickler API.

Dataset Selection

This dataset is available upon request in full or summarized form, under a data sharing agreement. A complete summation of the dataset is also available in report form.

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As will be discussed in another presentation:– We represent the data within a semantic model which

expresses relationships within the social network

– We define this model as Fine-Grained User Diffusion (FGUD)

– This model allows for analytic traversal at the user level

– Sample: (:Post attribute)

Dataset Storage Schema

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“Simple” Language Analysis– K-Means Clustering of Shannon's Entropy

• Language Agnostic Calculation [9, 10]

• Represent the calculated entropy of each message in the dataset as a 1-dimensional array in R and compute the initial graph

Entropy K-Means

Analysis

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● Entropy scale 1-8● Previous work has shown that Twitter has 3 distinct

groups: Human, Bot, Cyborg

Analysis

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Allowing K-Means automatic cluster number selection, we get 27 distinct groups:

Analysis

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Gender Detection– Both, name (if known) of author, and message content

is used

– Utilizes a Naive Bayesian classifier based on Mustafa Atik, and Nejdet Yucesoy’s, (Genderizer) [13]

• Gender was determined for 82.05% of all messages

– Did not use S. Sakaki, et. al method combined gender inference due to the 6 fold increase in computation for 0.48% increased detection

Analysis

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Time Zone subdivision– Dataset contained only 0.116% geo-tagged

– Cheap Geo-inferencing

– Concentrated on only US Time Zones

– Broke into Male/Female for each

Analysis

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Still working to impliment: M. Conover et. al. work “Predicting the Political Alignment of Twitter Users” [15].

– This is a TF-IDF (Term Frequency – Inverse Document Frequency) method

– Allows categorization of “Left” and “Right” affiliations

– This method has not been implemented on data subsets like ours: (human only, gender, and geographic specific)

Analysis (Group Affiliation Issue)

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M. Conover et. al. work only addresses network membership and use of specific hashtags

– Leaves out a number of scenarios: • Joining a network just to troll it or try to sway others

• Frequent communication with a group/network that they are not a part of, etc.

Analysis (Group Affiliation Issue)

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Presented a down selection approach to select posts Examined group affiliation detection and found that

work needs to be done in this area before methods can be implemented in order to lower inaccuracies

We are continuing this work currently– Traversing and collecting “snapshots” of all posts,

following/followed relationships, profiles at moments in time

– 1 complete snapshot of the same accounts each quarter for 1.5 years before and after the 2016 US presidential election

– Measuring resultant changes in individuals

Conclusion / Future Work

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For more information contact:

Joshua S. White

Josh@rsignia.com

References / Contact Info