Why is sentiment analysis important?

Today, the rapid spread of social media use, blog use and online shopping has enabled users to access a large number of interpretable thoughts over the virtual environment. Due to the rapid increase in data in the electronic environment, which produces 2.5 million bytes of data every day, the need to analyze these texts automatically increases in the same way. Therefore, Sentiment Analysis, which is a sub-research area of ​​Natural Language Processing (NLP) concept, emerges. Sentiment analysis enables companies to advance in automating all kinds of processes such as marketing analysis, public relations, product reviews, product feedback and customer service.

Sentiment analysis, also known as Opinion Mining, is the interpretation of a subjective language element, such as speech or writing, on a particular topic. Although there are many studies on sentiment analysis of texts in the literature, sentiment analysis of images gains importance with the increase in applications where image sharing is too much.

Types of Sentiment Analysis

Sentiment analysis aims to classify the text according to its subjective content. In general, texts are polarized positively or negatively, which is a binary classification. Some of the other types other than binary classification are as follows:

Fine-grained: In some problems, positive or negative evaluation may not be enough. In this case, fine-grained classification is used, and classes such as very positive, positive, neutral, negative and very negative are used. In addition, very positive and very negative expressions also allow evaluation of whether the speaker is angry or happy.

Emotion detection: Emotion detection aims to detect emotions such as happiness, sadness, disappointment, anger. In some systems, words showing the emotion of the text are used for emotion detection; these words vary according to the judgments of that society or the content of the data set created.

Objective-based sentiment analysis: In objective-based sentiment analysis, it is aimed to measure the satisfaction of people about a feature of that product, instead of detecting positive or negative thoughts about a product.

Sentiment Analysis Advantages

It is estimated that 80% of the world's data is unstructured. This data consists of texts such as emails, chats, social media comments, surveys, articles. It is very difficult to make sense of and analyze these data.

Sentiment analysis models aim to minimize manpower and achieve clearer results by automating the business processes of companies and extracting meaningful information. Thanks to an automatic system, it is possible to eliminate the human factor making mistakes.

Scalability: Manually editing thousands of tweets, customer support calls, or customer comments is hard to imagine. Sentiment analysis allows data to be scaled and processed efficiently and cost-effectively.

Real-time analysis: Sentiment analysis can be used in real time to identify critical information during a given situation. An instant event on social media or situations related to an angry customer can be given as examples. It is of great importance to determine the situation and intervene quickly.

Consistent criteria: People may not be able to make a consistent assessment when determining the sentiment of a text. It is estimated that when different people evaluate the emotion of a particular text, only 60-65% of them make the same classification. Sentiment analysis is a subjective field that is heavily influenced by people's experiences, beliefs, and thoughts, but automated sentiment analysis systems reduce errors and increase data consistency.

Sentiment Analysis Challenges

Researchers are faced with some difficult situations due to language difference, lack of data and noisy data.

• The basis of automated sentiment analysis is based on the use of large data sets. In order to use the sentiment analysis system and put it into action, it is necessary to have a large data source and volume. When there is not enough data to feed the system, it is inevitable for the model to make an erroneous evaluation.
Since sentiment analysis aims to make sense of subjective texts, the created texts can be written in everyday language or without paying attention to grammatical rules. In addition, the texts may contain metaphorical and sarcastic expressions. In addition, data capacity is limited in describing various human emotions such as anxiety and fear.
• Multilingual texts are also a challenge. While it is possible to reach a lot of data and research especially in English, the number of resources in other languages ​​is very low. Each language has its own sentence structure, words showing mood and metaphorical expressions. In this case, the sentiment analysis models created also differ in the same way. In multilingual texts, determining the language of the text and developing a language-appropriate model can also be a step.
• In order to create a model that can provide a solution to the problem, such difficulties must be identified and the system must be fed using a sufficient number of effective datasets.

Probleme çözüm getirebilecek bir model oluşturabilmek için, bu tür zorlukların tespit edilip sistemin yeterli sayıda ve etkili veri kümesi kullanılarak beslenmesi gerekir.

Sentiment Analysis Usage Areas

Sentiment analysis contributes to the background evaluation of live chat channels. This technology determines the emotion behind the text and enables the evaluation of customer satisfaction. Sentiment analysis systems are the most effective approach in determining the strategies of many companies today.

• It is much more likely to persuade happy customers to buy a new model of a product they are planning to buy. On the contrary, it is possible to avoid driving disgruntled customers away with undesirable sales steps. Sentiment analysis is also effective in evaluating satisfied or dissatisfied customers.
• Evaluating customer representatives through chat channels is also of great importance in terms of providing quality service. With sentiment analysis systems, the attitude of the customer representative towards customers can be evaluated.
• Sentiment analysis models, which are trained to evaluate customer mood, also provide quality service. Analyzing the customer's emotional state is also helpful in creating effective marketing materials.
• Evaluating whether the customer is satisfied with the product is also decisive in producing more effective and useful products. At the same time, it is an important value to try to increase the appreciation of the company by determining whether the general opinion about the company has decreased from the customer's feeling.
• The sales representative may be very good at providing a good service and offering the right product to the customer; By means of sentiment analysis systems, determining the most appropriate approach for the customer and providing customer-specific service will enable to achieve clearer results. It also enables the business to identify its strengths and weaknesses.
• Sentiment analysis can also play a helpful role in evaluating customer complaints.
• Collecting and analyzing data from social media is a very good way to go, as people are comfortable expressing their true opinions about their experiences with various products and brands on social media. Thanks to social media tools, it has become very easy to find answers to questions such as how customers praise the product or what feature of the product they complain about.
• Crisis management is also possible through social media. Thanks to the sharing of many users about product dissatisfaction, accessing data instantly and producing a solution contributes to the crisis management of sentiment analysis.

Sentiment Analysis Solution Methods

Classification techniques are firstly divided into two as machine learning and data dictionary based. In data dictionary-based classification, it is aimed to create the sentiment analysis dictionary used to analyze the text. This dictionary is a collection of words with positive or negative emotions. Because these expressions are created by hand, words that express the emotion of the text may be missing. One of the disadvantages of using dictionaries is that the way people express their emotions varies. It will be very insufficient to create such a data dictionary for expressions containing allusions and metaphors. Data dictionary-based approaches do not need very large datasets to obtain a dictionary, so it can be a meaningful method when data is scarce.

Machine learning approaches require being more powerful in terms of data, that is, having enough data for the system to learn depending on the related problem. It is generally divided into two as supervised and unsupervised. Artificial Neural Networks, one of the supervised learning methods, should be modeled using much more data than other supervised learning methods. Therefore, artificial neural networks have become one of the most studied areas with the ease of accessing big data thanks to today's technologies.

References:

1. https://www.whoson.com/customer-service/top-ten-benefits-of-sentiment-analysis/
2. https://monkeylearn.com/sentiment-analysis/
3. https://www.analyticsinsight.net/benefits-of-sentiment-analysis-for-businesses/
4. Liu, B. 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool, 143, San Rafael.
5. Medhat, W., Hassan, A. and Korashy, H. 2014. Sentiment analysis algorithms and applications: a survey. Ain Shams Engineering Journal, vol. 5, 1093-1113.