When we read an article, a social media post, an email feedback, or even an official company document, we instinctively feel its sentiment despite not paying too much attention to the positive or even negative terms mentioned in it. What’s more, later on we can still call the range of emotions to mind we had felt reading the given text content.
But what about texts we haven’t read before? Without knowing their content, we can’t decide what sentiment they convey. And what if we are interested only in positive or even negative news about a topic, a phrase or a keyword (even about the name of our company)? In this case, we definitely need a special, automated text analytics solution that can determine the sentiment of the given text content.
What is sentiment analysis for?
Sentiment analysis is required when the goal is to determine the sentiment of the relevant text contents, and to get acquainted with the positive or negative sentiment of the search results found regard to a particular keyword search.
What processes can be done by sentiment analysis? (What processes is sentiment analysis beneficial for?)
- examination of the sentiment of opinions about a brand, product, service or an optional expression, and monitoring the changes in sentiment values
- filtering the search results by sentiment values
- preparing analyses and evaluations by the help of sentiment filtering
How it works?
At the first step it is essential to determine which content is relevant with regard to sentiment analysis. After gathering these contents, the sentiment analysis can be started using sophisticated linguistic methods. Fine-tuning option is also available to refine the obtained results. Based on the sentiment values, the text contents are categorized into predefined sentiment groups (from very positive to very negative).
Besides the categorization of contents the sentiment-based filtering of the results can also be realized, since these groups can function as filtering options in the search engine.
How does it work in practice?
We can provide turnkey solutions for each step of the process described above. Let’s take these step by step.
First, the determination of relevant documents and online contents is required regarding the sentiment analysis. If web-based content analysis is also needed, it can be collected with the TAS Data Collector solution. The next step is the automatic tagging process of these text contents.
The TAS Tagger is able to extract and define key phrases and topics (tags) from textual contents. These terms and entities (person names, places, organizations, dates, etc.) are identified using computational linguistic methods. TAS Tagger’s integrated sentiment analysis technologies include solutions from Google, IBM, Microsoft, Rosette and Neticle. The customer is free to choose from these depending on the language of the text content and the goal to be achieved. It is worth mentioning that in addition to sentiment analysis, IBM and Neticle emotion analysis modules are also available in the TAS Tagger.
The service is able to recognize the expressions that influence (affect) the sentiment and determine its positive or negative value and thus implements the categorization of the given content.
In addition to the automated solution, the user can accept or even reject the sentiment values generated for a given content in the TAS Tagger interface.
Filtering by sentiments in the search engine
If a search solution is already used in the enterprise IT environment, we can provide the sentiment analysis of the contents as metadata.
In case a special enterprise search solution is required due to unique user needs, we offer TAS Enterprise Search, which allows the user to compile advanced queries, as well as to apply special filtering options, even based on sentiment values. In the TAS Enterprise Search UI the obtained sentiment values are displayed and divided into 5 groups as filtering options:
- very positive
- positive
- neutral
- negative
- very negative.
On the one hand, by filtering the results based on their sentiment categories, documents with the same sentiment rating can be examined separately, and on the other hand, the indicated number of results by values can be a direct basis for further statistical analysis.
Modern analytical tools for making the right business decisions
Today, several major global companies are already taking advantage of the information provided by sentiment and emotion analysis. Among others, they analyse market reactions like posts on social media, blog articles, as well as publications, for example, in case of launching a new product. The method is also used to monitor and analyze the competitors.
With the help of sentiment analysis, we can come into possession of information that has not been possible to measure so far, or has only been possible with a huge amount of time and energy. By now, with the significant improvement in the efficiency of artificial intelligence, machine learning methods and computational linguistic tools, this information has become available also to small to medium-sized enterprises, helping business decision-making Arrive at a new level.