Sentiment Analysis in Social Media

For today’s organizations, social media has become an irreplaceable business tool, a must for any online presence, and an important opportunity for conversation with customers. To make the best use of social media, your company needs to listen and really understand every interaction that takes place in the social media space.

What is Sentiment Analysis?
Sentiment analysis identifies and categorizes opinions in a message in order to determine whether they are positive, negative, or neutral. In social media analytics, sentiment analysis is a form of social listening that measures the tone of conversations that take place on social media.

Why is Sentiment Analysis Useful?
Without sentiment analysis, social media metrics cannot tell the full story. Sentiment analysis helps you learn the ‘why’ and ‘how’ aspects of the messages. While other metrics can tell you what a message is about, you need sentiment analysis to explain the reasons behind the message.
Sentiment analysis can contribute to your organization in three ways:

Hear your Customers’ Authentic Voices
Sentiment analysis helps you to:
• understand people’s feelings about your brand, company, or solutions
• gain the trust and loyalty of customers by reacting to customer feedback in a timely and effective manner

Manage Online Reputation and Brand Image
Sentiment analysis helps you to:
• obtain a quick overview of your brand
• respond proactively to any issue and prevent possible crises that can damage your brand’s reputation

Measure the Effectiveness of Marketing Efforts
Sentiment analysis helps you to:
• measure the success of your marketing campaigns effectively
• monitor the sentiment level of competitors and reshape your positioning accordingly

The Challenges of Sentiment Analysis
Every conversation has layers of meaning that can be interpreted differently from person to person. Since it is not always easy for people to classify a message according to the sentiment it reflects, it is not easy in sentiment analysis, either. Although there are a number of automated tools, they provide varying rates of accuracy. The higher the accuracy rate of a sentiment analysis tool, the better results for your company.

What affects accuracy?
Although there are dozens of social media analytics solutions, they differ in their technical capabilities. An analytics solution that features more intelligent technology will deliver more accurate performance for your company. The accuracy rate dependent upon the solution’s sentiment analysis algorithm.
Natural language processing algorithms provide effective solutions in terms of accuracy. Rather than dealing with individual words, natural language processing algorithms treat text as a whole, taking into account the hierarchical structure of language in which words constitute phrases, phrases constitute sentences, and sentences constitute ideas. When combined with machine learning technology, natural language processing algorithms can provide continuous development and improvement of sentiment analysis.

An effective option
SESTEK SocializerVox combines web analytics and natural language processing technologies to allow companies to track social media and helps construct a unified view of multichannel customer interactions. SESTEK SocializerVox includes a sentiment analysis algorithm that examines and classifies social media messages to determine whether they are positive, negative, or neutral. Sentiment analysis uses a neural network approach to machine learning. A neural network is a computer system modeled on the human brain. By using this system to process information, SESTEK crafts tailored artificial intelligence models for its customers to use social media analytics to monitor what people are thinking and saying about their products and services.

For more information about how SESTEK SocializerVox can deliver your company the social media analytics that it needs, please contact the SESTEK Sales Team at sales@sestek.com.

Author Hilal Bakanay

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