Business Insights that help you grow. The most actively growing part of the web is social media. With the rapid increase of opinion rich blogs, reviews, ratings, recommendations and other forms of online expression, online opinions equips enterprises with real insight into what is driving people, and what they really think in real-time.
What is Sentiment Analysis Solution
OLSPS Analytics’ Sentiment Analysis Solution is a natural language processing (NLP) model designed to reveal valuable information on current market sentiments. Through text mining and computational linguistics our solution is able to analyse extensive amounts of text on social media platforms and derive opinions towards certain subjects, items or events. In addition, Sentiment Analysis Solution goes one step further and establishes the source generating these sentiments.
Benefits of the Sentiment Analysis
Benefits of Sentiment Analysis in Commercial Sector:
A company may benefit, in many regards, from deeper insight into public opinion. For instance, firms with positive feedback can use our solution to continuously monitor customer satisfaction and their perception of different goods and services provided by the company.
Alternatively, in the case of companies with negative market sentiments, identifying the key sources to these attitudes, Sentiment Analysis can help address these issues with the highest level of efficiency by optimally allocating resources to resolve the problems.
Benefits of Sentiment Analysis in Government Sector:
Sentiment Analysis has become very popular solution for political campaigns. Even though short text strings might be a problem, sentiment analysis has shown that Twitter can be seen as a valid online indicator of political sentiment. Tweets’ depicting political sentiment demonstrate close correspondence to parties’ and politicians’ political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape.
How does Sentiment Analysis work
OLSPS Analytics Sentiment Analysis Solution gathers text from various sources (for example social media) and places them into a structured database where they can be used for analysis. This raw collection of data is stored in the form of text, which is known in the analytics world as ‘unstructured data’; data which is typically very difficult to use in any analysis. However, the solution developed by our team uses Text Mining, a set of intelligent algorithms, to derive meaning from this unstructured data.
A simplified description of Sentiment Analysis process:
Sentiment Analysis Solution Case Study
Nedbank is one of the four largest retail banks in South Africa based on the number of customers. With such a large customer base, it becomes critically important to monitor the customer sentiment at any point in time. With this in mind, Nedbank approached our team to help them to develop a social media monitoring tool based on IBM SPSS software.
Sentiment Analysis Solution tool enables Nedbank to collect data on the online interactions that the company has with its clients across a variety of different social media feeds including Twitter, news feeds, blogs, Facebook and other relevant sites. The online interactions are then categorised and turned into ‘structured data’ (through the process described in the section above) which are then used for further analyses. In this specific scenario, the data is used in the Nedbank solution to drive customer engagements and upsell opportunities. There is also potential for the data to be used in predictive models to drive customer retention and other goals.