Out of the Dark Ages: The Rise of Social Media Sentiment ......definitive social media sentiment score would be applied, thus limiting an organization’s understanding of consumers’

Out of the Dark Ages:The Rise of Social Media Sentiment Analysis

Out of the Dark Ages: The Rise of Social Media Sentiment Analysis 1

Introduction

The Dark Ages

The Enlightenment

Sentiment Analysis Checklist

The Renaissance61016

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2Out of the Dark Ages: The Rise of Social Media Sentiment Analysis

Introduction

The Dark Ages

The Enlightenment

Sentiment Analysis Checklist

The Renaissance

Over the past five years, sentiment analysis has become

an increasingly integral part of social media management for enterprises of all sizes. No matter what industry or vertical an organization may be in, it is imperative to gain a secure hold on how people and consumers are receiving company messaging and product information. As social media continues to prove itself as an unbiased and convenient forum for individuals to express their

emotions, sentiment analysis is the key to understanding how various demographics feel about a certain product, brand, or experience. In this paper, we take a look at the history of sentiment analysis in social media, a period we have deemed The Dark Ages. Then we move on to the current use of sentiment analysis, which we call The Renaissance. Finally, we give a preview of the future of social sentiment – The Enlightenment.



The Rise Of Social Sentiment Analysis :



During the past two years, the social media industry has experienced incredible growth in the scoring and functionalization of sentiment analysis. Sentiment can be

defined as a view or attitude towards a situation or event, and refers to people’s feelings and opinions on a given subject. Measuring and quantifying sentiment surrounding an industry or brand is essential for an organization to gain a secure hold on how people are truly receiving a brand’s messaging, products, or overall voice.

By looking at obvious numbers such as the amount of company page likes, number of followers, and volume of interactions received on owned media content, brand managers would compare their social impact with their competitors’ numbers. But the industry would soon learn that high-level statistics such as follower counts are often irrelevant and misrepresentative, as someone can follow a company on social media without showing any further interest in what that company is saying or doing.

Beyond the inaccuracy of only looking at non-descript statistics, during the corporate social media Dark Ages the approach to measuring sentiment was one plagued by the use of solicitation. Brands would openly and directly ask consumers and people with interest to an industry about their sentiment regarding brands and campaigns via surveys and focus groups. While this was thought to be useful at the time, and insights into general audience reception were yielded, with this method comes the unavoidable obstacle of overcoming biases.

The Dark Ages

In the early days of organizations using social media to push business further, every aspect of capturing and implementing sentiment scores into corporate initiatives was a manual and solicited process.

In The Beginning…

Beyond The Obvious

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Eventually, managers within the enterprise came around to recognize the importance of measuring and recording the sentiment of the individualized, consumer-produced posts

surrounding their brands. This process was excruciatingly manual, as a trained representative would have to physically dig through each post and tag it as having positive, negative, or neutral implications. There were no text-analysis algorithms available that could accurately score the sentiment of posts, and therefore no easily obtainable and summarizing reports to pass up to the C-level. It wasn’t until later that the process of sentiment scoring, recording, and reporting became more automated and streamlined, and social media sentiment analysis emerged from The Dark Ages into The Renaissance, where the technology began to catch up to the market’s needs…

The Dark Ages

The Technology Bottleneck




The Rise Of Social Sentiment Analysis :




While a post may have been comprised of both positive and negative expressions, only one definitive social media sentiment score would be applied, thus limiting an organization’s understanding of consumers’ true perception of their brand.

During The Renaissance, sentiment analysis in social media was manual, slow,

inaccurate, and limited heavily by the technology readily available in the marketplace. But things were changing rapidly… Once the concept of sentiment analysis had been legitimized and adopted throughout the social media industry, significant strides were made to automate the scoring, recording, and reporting processes. The biggest enhancement of sentiment analysis rested on the shoulders of machine-learning algorithms.

By 2013-2014, algorithms being utilized by major SMMS platforms yielded about 70% accuracy when it came to identifying posts as positive, negative, or neutral. If an organization wanted to increase the accuracy beyond that, a skilled representative would train the system by sorting through a significant number of social posts, and manually scoring them until the algorithm had enough data to refine its definition of positive, negative, or neutral sentiment. The new wave of machine-learning was significant for the evolution of sentiment analysis, as algorithm-based sentiment scoring only grew more accurate depending on the number of manually adjusted post scores. Analysis at the post level meant that each piece of content received an overall score. While a post may have been comprised of both positive and negative expressions, only one definitive social media sentiment score would be applied, thus limiting an organization’s understanding of consumers’ true perception of their brand.

The Rise of Machine-Learning

The Renaissance



Limitations: An ExampleIn the following example Tweet, we address the limitations surfaced by early machine learning.

At this time, the limits of generalized sentiment analysis were consistently present,

as there were only three levels of scoring – positive, negative, and neutral. As shown in the example above, social posts can express mixed tones that would be misinterpreted by traditional sentiment tracking engines. A traditional system would see the words “wonderful,” “thanks,” “better,”

Mc Jagger @radioflyer87Wonderful. Delayed flight again. Thanks @XXair. Always delayed, but your service is getting better and love the legroom.

and “love,” and could ultimately score this post as positive, when in reality it is actually expressing a very mixed experience (both positive and negative). The majority of current social sentiment tracking systems can’t process this type of content. Further, if the post had been measured manually, the user would have to either assign an inaccurate overall score, or simply ignore the post.

The Renaissance

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Brands Need More Control

In addition to the language limitations, brands needed a way to categorize sentiment across different

categories that were important and unique to their organizations. The majority of platforms on the market only provided basic machine learning sentiment coupled with limited filtering functionality. Brands demanded greater granularity and segmentation of sentiment data. They needed a structured framework to address sentiment in key areas that each brand needed to measure. For instance, a CPG brand would benefit from looking at packaging, taste, ingredients and other categories, while an automotive company would have far different categories of interest such as comfort,

handling, service, and various others. The generalized sentiment score did not tell an accurate story, at least not until that score could be broken down further. In addition, many of these tools were standalone offerings that required jumping between different platforms, pulling from disparate data sets. While this was a partial solution, sentiment without context was still inaccurate. But these hurdles would soon be obsolete…

“Brands demand greater granularity and segmentation of sentiment data.”

The Renaissance



The Rise Of Social Sentiment Analysis:



The shift from scoring overall

posts to scoring individual entities

allows users to analyze the

sentiment of multiple terms

and phrases more reliably and

accurately.

The desire to measure social sentiment at a more granular level has risen rapidly within the industry. How do you gauge the

sentiment of a long-form post or even a Tweet conveying mixed signals? How can you group terms and phrases together to gain a greater understanding of sentiment within unique and flexibly defined categories relevant to your industry and company? A better, more accurate scoring mechanism was needed. That mechanism is Sentity.

The process of scoring, recording, and reporting sentiment on social media has been changed dramatically with the introduction of Tracx’s Sentity engine. Sentiment classification is no longer based on language cues across the entire post, as users can now define specific keywords and phrases for sentiment tracking called “entities.” With Sentity, greater accuracy and deeper analysis are gained by looking at smaller pieces of language surrounding each mention of these user-defined entities. In addition, entities

The Introduction of Sentity

Entities are defined as a term representing a single person, place, or thing, about which data can be stored.

The Enlightenment

can be grouped into larger categories for greater insights into key focus areas for brands.

Entities are terms representing a single person, place, or thing, about which data can be stored. Entities maintain distinct and separate existence and objective, can be classified, and have stated relationships to other entities. This shift in focus from entire pieces of content to individual entities within each post allows users to analyze the sentiment of multiple terms and phrases within a single piece of content to uncover truly actionable, focused insights.



For instance, in the example previously examined (see above), a traditional system would see the

words “wonderful,” “thanks,” “better,” and “love,” and could ultimately score this post as positive. This method ignores the evident sarcasm and other influential pieces of the post such as the “service getting better” or the “love” for the legroom.

More importantly, by layering in the use of definable entities, and by tagging the “delayed flight” portion of the post as having a negative sentiment within a “flight status” entity, an organization can then start to gain deeper insight into more granular categories. The Sentity engine provides easy measurement into key areas of a brand, not just an overall score. The above-cited example could have contained more complex messaging, and actually could have been tagged across multiple entities based on user-defined criteria, yielding highly accurate sentiment scores directly aligned with an organization’s KPIs.

By compiling a complex sentiment score through identifying specific entities within the entire post, generalized and inaccurate classifications of expressive tone across a whole post become a thing of the past… Good riddance to vague and inaccurate methods!

Example: How Sentity Creates Greater Accuracy

The Enlightenment

The Tweet expressed a very mixed sentiment that simple sentiment scoring engines would measure incorrectly, and the weight of each measure would become subjective depending on the focus/needs of the brand. Accordingly, the final sentiment score would be skewed. With Tracx’s Sentity engine, granular measurement of sentiment at the entity level becomes possible, producing a highly accurate sentiment score of the post and any key entities being measured.

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Mc Jagger @radioflyer87Wonderful. Delayed flight again. Thanks @XXair. Always delayed, but your service is getting better and love the legroom.




Sentiment analysis prior to Sentity measured content on

an extremely broad level via an overall score of the content within a post. Scores did not accurately reflect the true social media sentiment surrounding a brand or term. Sentity surpasses this limitation, as different words and phrases within a post, or “entities,” are measured individually for sentiment. Then, these “entities” are further categorized into user-defined focus categories, creating more accurate and reliable sentiment measurement.

The Enlightenment

Sentity allows sentiment tags, and the overall breakdown of data, to be more fluid and customizable. Sentiment scores can now be immediately pivoted and sorted based on the entities defined by the user. The sentiment criteria is not locked, so it shifts dramatically depending on what data the users choose to focus on. For instance, CPG brands and automotive brands have distinctly different entities / categories that they wish to focus on and measure more acutely.

Uncovering the True Value of Sentiment Analysis



The Enlightenment

A Unified Approach to Social Media

Sentity gives users across multiple departments the power to adjust the sentiment association of terms and phrases, substantially increasing accuracy

to reveal a level of insight previously unavailable to the market. As part of the Tracx platform, these insights come with a unified, holistic view of the entire social media ecosystem, including demographics, analytics, listening, customer care, crisis management, customizable reporting, and real-time engagement. Industry leading analytics and engagement now includes the industry-leading sentiment analysis engine, Sentity.

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Choosing A Social Media Sentiment Analysis Platform

Can you see sentiment analysis on the channels you publish content to, the content you publish, the comments you receive, as well as the individuals who make comments?

Can you isolate terms to score sentiment at an individual entity basis for deeper, more accurate insights?

Are there easy-to-read dynamic sentiment visualizations that enable you to dive deeper into the sentiment analysis at a post level?

Are you able to provide multiple sentiment scores to a single post if needed?

Can you edit the sentiment keywords and scores to fit your business needs?

Can you filter by sentiment across topics, demographics, regions, and other key metrics?

Can you set alerts based on sentiment of trends or of individual posts with custom interaction levels?

Do reports contain sentiment monitoring for both comments and users?

Can you edit sentiment for individual comments?

Can you drill down to report on sentiment of individual accounts within channels?

Can you export data to excel?

Can you view reports aggregated across all social channels?

Can you drill down to the channel-specific level?



Acknowledgements

Contributors

About Tracx

Ben FoleyMarketing Coordinator

Reinhardt SchuhmannProduct Manager

Kate LingDesigner

Tracx is the next generation social enterprise platform that empowers brands to manage, monetize, and optimize their business. The technology refines and analyzes masses of data across all social channels, providing deep insights into customer, competitor, and influencer behaviors. It delivers the most relevant, high impact audiences and conversations by capturing a 360-degree view of activity around a brand, product, or ecosystem. With Tracx, businesses obtain geographic, demographic, and psychographic insights to identify and target influencers, improve planning, enhance monitoring, and effectively focus engagement. Tracx is headquartered in New York City with offices in Tel Aviv and London. The world’s top brands rely on Tracx.


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Out of the Dark Ages: The Rise of Social Media Sentiment ......definitive social media sentiment score would be applied, thus limiting an organization’s understanding of consumers’