Sentiment analysis is essentially an analysis of a text from which certain information is extracted. In the future, it will be analyzed. In the course of this analysis, the attitude of ordinary users to the brand or service is investigated. One of the most popular ways to analyze mood is to track messages on social networks, however, it is a bit superficial, and it is wrong to draw conclusions using only one method.
Modern technologies allow for more in-depth analysis, if you need a custom ML software system, here is a great team I was working with. The combination of certain advanced methods will become an effective tool in conducting research. All user messages of customers or buyers can be classified into 2 types, namely:
1. Criteria that interest customers,
2. Customer reactions and intentions in accordance with these interests.
Basic Text classifiers
The simplest and most popular method of analysis. Incoming messages from customers are analyzed and their mood is determined (clearly positive or negative, as well as a neutral shade).
When using this tool, the user’s intentions that come from his message are analyzed directly. The type of message (news, interest, complaint, etc.) is also determined directly.
Contextual semantic search (CSS)
To obtain certain information in this analysis, it is necessary to understand which aspect of the brand (service) the user (consumer, client) will talk about.
There is a special algorithm called contextual semantic search. The principle of its operation is to receive all incoming messages, which are further analyzed, and this information is used as initial data. This format is the most effective and significantly exceeds all existing types of analysis.
The analysis of these messages from various social networks can provide certain data about the attitude to the brand or service. Most companies use this particular method of analysis. However, the larger the user base, the more messages need to be analyzed, and this greatly complicates the analysis and reduces its accuracy. That is why it is necessary to further systematize the data using CSS.
Thanks to the information obtained using CSS, the company (brand) gets an idea about the attitude of users to their services (goods).
A concrete example will allow you to better understand the essence of this analysis. Uber, which is the most interesting global startup, is one of the founders of this type of analysis. This company is famous all over the world because it is represented in more than 500 cities that are located around the world. In such conditions, it is quite difficult to track the reaction of consumers to the company’s services. A living person (even a high-quality specialist) will face the problem of tracking all messages and analyzing moods, but artificial intelligence will perfectly cope with this function, in which special algorithms will be used.
For a more specific assessment of this example, it is necessary to go directly to the numbers. Thus, during the analysis, comments were taken to the official pages of the company in the largest social networks, during which the following comments on Facebook (34,173 pieces) were to be analyzed:
- Comments on Facebook (34,173 pieces),
- Tweets on Twitter (21603 pieces),
- News (4245 articles of various nature)
The analysis of these messages and news can give the general attitude of users to the brand. This analysis will bear an exclusively superficial attitude. To fully understand current affairs and consumer sentiment, it is necessary to delve into the qualification of these messages in more detail using contextual semantic search.
Most of the messages under consideration are negative, but one factor is liked by many users, namely the price. Considering that this social network contains an abundant amount of advertising, spam, and other messages that should not be paid attention to, the number of positive comments regarding the price is significantly reduced, which allows the brand to understand the true state of affairs in this market and allows it to make adequate decisions regarding its pricing policy about the attitude of the end consumer to the image of the company.
Messages in this social network were also subjected to this analysis. When analyzing messages without filters, it was difficult to draw unambiguous conclusions, but with the help of CSS, this information became easier to qualify. During this analysis, it turned out that most of the problems (complaints) of users were directed at the work of drivers who canceled the train (users paid for the trip at the same time). For such a large company as Uber, this information allows you to find errors that exist in your system. Analysis of this kind of data will allow you to further exclude errors and inaccuracies in the operation of the application. The elimination of these errors ultimately leads to an increase in the company’s rating in the eyes of the end consumer, which will further contribute to the growth of demand for the company’s services and, as a result, an increase in profits.
The main topic that was most relevant in various news headlines was the problem of security. CSS allows you to evaluate news by its relevance, which allows the company to focus on particularly popular issues. Solving key business problems is fundamental for any company. It is the news that allows us to understand the problems and moods of citizens in more detail, but it is quite problematic for a person to monitor this array of information daily since a large number of various articles are published daily, in which the same aspects are described from completely different sides. Artificial intelligence allows you to evaluate all the news in more detail, as well as to make a correct decision that concerns certain problems.
The development of technologies and social networks has allowed large companies to assess in more detail the quality of their services, as well as the attitude of citizens to the image of the company. CSS allows you to evaluate the opinion of users about the company, while not taking into account various advertising reviews and spam comments, which often have a strong impact on the final reality. Most successful companies already use this method of analysis to display the real opinion of users about their services and image. AI uses modern analysis algorithms when analyzing, and also regularly improves these algorithms to provide more complete and truthful information that concerns both the image of the company and the quality of the services it provides and the goods it sells.