Sentiment analysis features

To be successful and pertinent businesses have to constantly collect feedback from their customers to assess customer satisfaction with a product or service and enhance them if possible. Similarly, the consumers who make a purchase often feel the need to get an independent evaluation of such products.

With sentiment analysis, an array of texts, comments, messages, survey responses, and any other textual data may be studied to determine their emotional coloring – positive, negative, or neutral. Sentiment analysis helps companies to further improve their products and services as well as consumers to understand whether they want to buy the product or not. Let's take a look at what sentiment analysis is and why it's so helpful.

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What is Sentiment analysis?

Sentiment analysis, also described as opinion mining, is one of the machine learning (ML) domains that allows one to determine the emotional coloring of a particular text or sentence. It can be applied to retrieve textual data and classify it into specific types of sentiment for convenient use.

Sentiment means the emotional attitude of the creator of a statement towards something, expressed in the text. The sentiment of the entire text may be defined as the general sentiment of its individual elements or sentences. Here are the basic types of sentiment evaluations:

• positive sentiment;
• negative sentiment;
• neutral sentiment or no emotional coloring. The basic challenge of sentiment analysis involves classifying the polarity of a given text, meaning deciding whether the opinion expressed therein is positive, negative, or neutral. The aggregate of positive and negative words is then analyzed to figure out what type of words prevail in the submitted document or array of texts.

Natural language processing (NLP) is at the core of the sentiment analysis concept. That is the Artificial Intelligence (AI) subset handling human language, enabling computers to comprehend texts and make use of text processing. Artificial intelligence systems may be tailored to the specific tasks of natural language processing. One of those is sentiment analysis.

The sentiment score is one of the ways how sentiment can be assessed. Diverse aspects, like the range and sort of emotions conveyed, the intensity of the emotions, as well as the specific context in which they have occurred, are considered in the sentiment score calculation.

The main purpose of sentiment analysis

Sentiment analysis or opinion mining describes a method of content analytics the major purpose of which is to classify a text according to its sentiment by detecting opinions in a text and revealing their properties. It is up to the task at hand to determine which properties will be investigated.

The sentiment analysis enables the extraction of the author's opinion on a given object from the text data. This opinion may express the author's judgment, view, or evaluation, his or her emotional state. The author, that is, the individual who shares the opinion, may also be the target of the analysis.

Approaches to sentiment analysis

Generally, there are 3 primary approaches to sentiment analysis:

Rule-based approach

This method is based on linguistics, with a set of rules developed by professional language analysts. Such an approach implies the existence of a dedicated vocabulary that contains words or collocations. The rule-based approach identifies, classifies, and evaluates specific keywords based on the pre-defined tone values for such words in the vocabulary.

A sentiment score and, sometimes, sentiment strength are noted for each word in the vocabulary. The software automatically checks the sentiment score to find positive and negative sentiments and to evaluate whether a sentence is positive, negative, or neutral and then sums up the sentiment score. The final grade is compared with the set boundaries to detect the overall sentiment.

Machine Learning-based

A machine learning algorithm that is more commonly applied in sentiment analysis is supervised learning. Such algorithms employ automatic feature extraction from a text to perform sentiment classification. The essence of the approach lies in the fact that an ML classifier is initially trained on the labeled data and then applied to classify new texts.

Hybrid approach

This type of analysis enables several approaches to be employed simultaneously. For instance, a hybrid approach may operate through a combination of machine learning techniques and rule-based systems. It utilizes the capabilities of both modes to improve speed and accuracy in delivering fine-grained sentiment analysis.

Types of sentiment analysis

Intent sentiment analysis

This kind of analysis lets you know whether the customer is eager to make a purchase or is exploring a product. For clients who are keen on buying, the company's specialists arrange special campaigns for them or determine whether it is worth contacting the potential client later with special offers and discounts.

Emotion sentiment analysis

Emotion sentiment analysis involves discovering a person's emotions. Keywords are matched to emotions, yet this is a more challenging task since the computer tries to interpret different human emotions when analyzing sentiment. Emotions in texts do not always have an unambiguous interpretation, because a person who wrote it may, for example, use irony, sarcasm, or multivalent words.

Aspect-based sentiment analysis

Aspect-based sentiment analysis describes a subtype of sentiment analysis designed to identify attitudes about a particular aspect of the underlying subject matter. It includes the extraction of aspects or attributes, the prediction, and classification of sentiment polarity, as well as the aggregation of sentiments.

Aspect-based sentiment analysis is a process that identifies sentiment in relation to various aspects. Aspects refer to qualities, characteristics, or features of goods or services. Analysis at the aspect level reveals more information about the author's attitude towards the aspects of the object, rather than the object as a whole.

The aspect retrieval involves the identification of these attributes through customer feedback. Then, experts perform polarity prediction and sentiment classification to decide whether the aspect polarity indicates negative or positive sentiment.

Data labeling for sentiment analysis

Simply put, when performing sentiment analysis, developers usually create their sentiment analysis lexicons. These contain details about which words and phrases are considered positive or negative sentiment.

The machine learning model for sentiment analysis is trained on massive quantities of carefully selected training data so that they can carry out complex operations such as emotional tone identification, key phrase and feature extraction, word frequency analytics, and overall sentiment detection to provide valuable information.

Machine learning sentiment analysis mandatorily requires the data to be labeled. A collection of all kinds of labeled content is available in the public domain, but more often than not businesses require their own customized labeling to meet their needs. Crowdsourcing can be a remarkably fast and convenient tool for labeling large amounts of data and building your sentiment analysis solution. For instance, a wide range of performers on Toloka manage to accomplish high-quality labeling in a relatively short period of time.

For accurate sentiment analysis, the labeling of data is of utmost importance, since this is the material for training and testing the sentiment analysis system. Without accurate data, it will not yield reliable results.

How does sentiment analysis work?

Supervised machine learning methods have become the most commonly applied approaches to sentiment analysis these days. Such methods consist of the initial step of training an ML classifier on previously labeled documents. The model is then applied to new documents.

To perform sentiment analysis with ML data scientists have to go through the following steps of the process:

1. Texts collection;
2. Text preprocessing or cleanup;
3. Data labeling, which consists of the specification of the correct type of sentiment for each text;
4. Selection of the classification algorithm and method for training the classifier;
5. The resulting sentiment analysis models are applied to determine the overall sentiment of the new collection of text materials.

Sentiment analysis features

In selecting sentiment analysis tools, there are some attributes worth paying careful attention to that will help you and your business get the complete and most accurate sentiment analysis to understand the public perception of different ideas and concepts or a newly launched product. These are some of the essential sentiment analysis features and strengths that a sentiment analysis tool should have:

Precision

Precision is probably one of the foremost metrics of sentiment analysis. Sentiment analysis tools or platforms have to be capable of accurately assessing manifested sentiments with varied degrees of accuracy. This depends on the level of text sophistication, but once trained on appropriate datasets, high-quality and accurate sentiment analysis should attain as much as 90% of accuracy.

Speed and scalability

Another equally important benchmark for evaluating sentiment analysis tool is its speed and scale. Simply put, sentiment analysis has to handle a great deal of textual data in a relatively short time. In sentiment analysis, speed means the rate at which the software can analyze a piece of content (news article, social media post, online review, video message, survey response, etc.) per second.

Personalization

A flexible sentiment analysis tool also should give users options to adapt its functionality to your industry-specific terminology, item titles, significant assets, and unique semantic subtleties. The type of sentiment analysis you opt for should be appropriate for your individual needs and the demands of the business sector you work in.

Sentiment analysis model

To make sentiment analysis work data scientists basically put instructions in the model. These instructions allow the model to learn. In other words, they build a machine-learning model, embedding a training algorithm in it. Now let's review which algorithms are more frequently employed in the creation of sentiment analysis model.

Sentiment analysis algorithms

Machine learning techniques have expanded considerably in recent years and are still evolving. As mentioned above, supervised learning is widely employed in sentiment analysis. The approach requires sentiment analysis datasets to be preprocessed. Then data scientists feed the system with labeled data, which is further applied to train the classifier, followed by its validation on brand-new texts.

One of the most accurate linear classifiers, the Support Vector Machine is frequently employed to perform sentiment analysis. Other classic algorithms to analyze sentiment are Naive Bayes Classifier, Decision Tree, and Logistic Regression.

Currently, various neural networks are more and more often applied to create sentiment analysis tools. For example, recurrent and convolutional networks. The development of the neural network sector, which entailed the introduction of large neural network libraries, has contributed to this.

Sentiment analysis challenges

Expression of irony or sarcasm

One major challenge for computers when applying sentiment analysis lies in the difficulty of identifying the exact mood of a text full of sarcasm or irony. This can cause analytical errors and potentially misleading results.

Changes in the tone of a word or expression depending on the setting

The multiple connotations of words that express judgment pose challenges in determining the emotional coloring of a text. Some terms may be neutral in one setting and have a different tone in another setting.

Changes in the tone of a word or expression depending on the context

Another problem of sentiment analysis is related to the situation when the original tone of a word or expression alters depending on the context of use.

Quality of the sentiment analysis solution

The quality of the ML solution is crucial because a poorly trained model can produce unreliable results and damage sentiment analysis performance. Therefore, it is vital to adopt high-quality models that have been trained on diverse and representative training data, as well as to carefully assess the outputs to verify that they are precise and robust.

Why is Sentiment Analysis Important?

The success of a product on the market relies on the customer's perception of it. Marketing specialists, product managers and public relations experts in a company cannot perform 24/7 social media monitoring and read all the existing reviews and comments about their products and services. It's simply not humanly possible. That's when a sentiment analysis tool comes to the rescue of companies. This way businesses survey their customers to build the appropriate image and brand reputation.

Sentiment analysis helps companies enhance their products and processes with genuine and relevant customer feedback. Sentiment analysis is sometimes applied by customers themselves, mainly when deciding whether or not to purchase a certain product.

On top of everything else, businesses have to quickly take action in case of potential downturns or emerging trends in today's fast-changing circumstances. Marketing experts rely on sentiment analysis to discover what customers think about a company's brand, products, and services and take immediate action based on their findings. The insight into consumer behavior may be a tremendous asset to companies seeking to highlight each consumer's unique personality and extend their experience with the company's goods.

Conclusion

Sentiment analysis is an essential and useful tool for business, as it gives companies the opportunity to discover what consumers really think about their products and services. Today, thanks to the rapid development of machine learning algorithms, such analysis can be performed quickly and cost-effectively.

Sentiment analysis may examine a collection of social media posts, articles, documents, and other textual data to assess whether they are positive, negative, or neutral in tone. Experts apply this analysis in a number of fields, ranging from market research and customer feedback review to social media monitoring and political surveys. Sentiment analysis may be applied in customer service applications, such as chatbots and mobile apps. It helps businesses to figure out what customer sentiment is concealed in every complaint, remark, and inquiry, letting businesses grasp deeper insights into their customers' preferences.