The human language is complex. Interpreting the tone of a piece of writing is vital to gain a conceptual understanding of the written words.
According to studies, 90% of the world’s data is unstructured. In other words, it is highly unorganized. Large volumes of unstructured business data are created every day in emails, support tickets, chats, social media conversations, surveys, articles, documents, etc.
It is challenging to analyze such large volumes of text for sentiment in a timely and efficient manner.
This is where Sentiment Analysis comes in. It is an automated way to help you scan through any free-form text for the user’s sentiments.
Sentiment analysis involves natural language processing, text analysis, and statistics to analyze customer sentiments. With the help of this tool, we can quickly determine whether a piece of writing is positive, negative, or neutral.
- What is Sentiment Analysis?
- What are the steps of Sentiment Analysis?
- How can it be used for various industries?
- What are the benefits of using effective Sentiment Analysis?
What is Sentiment Analysis?
Sentiment analysis means contextual data mining wherein you input a sentence, and it is categorized according to the underlying consumer emotions.
A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase.
When we look at any sentence, the human brain searches for sentiment-bearing phrases — that is, words and phrases that carry a tone or opinion and tries to interpret it, usually as adjective-noun combinations. We also draw from our previous experiences and accumulated knowledge to identify each sentiment-bearing phrase and analyse their negativity or positivity.
This is precisely how computer sentiment analysis works. It involves deep learning and machine learning techniques that “trains” the system to instinctively recognize nouns and phrases as “offensive” and categorize them accordingly.
What are the steps of Sentiment Analysis?
Sentiment analysis is a complex process that consists of 5 different steps to analyze sentiment data. These steps are:
1. Data collection: The first step of sentiment analysis consists of collecting data from user-generated content in blogs, forums, and social networks. These data are disorganized and expressed differently by using various vocabularies, slang, writing context, etc. Manual analysis is almost impossible. Therefore, text analytics and natural language processing are used to extract and classify such data.
2. Text preparation: It consists of cleaning the extracted data before analysis. The Non-textual contents that are irrelevant for the analysis are identified and eliminated;
3. Sentiment detection: The extracted sentences of the reviews and opinions are analyzed. The sentences with subjective expressions (opinions, beliefs, and views) are retained, and sentences with objective communication (facts, factual information) are discarded;
4. Sentiment classification: In this step, subjective sentences are classified in positive, negative, good, bad; like-dislike, but classification can be made by using multiple points;
5. Presentation of output: The main objective of sentiment analysis is to convert unstructured text into meaningful information. When the analysis is finished, the results are displayed on graphs like pie charts, bar charts, and line graphs. Moreover, time can also be analyzed and graphically displayed, constructing a sentiment timeline with the chosen value (frequency, percentages, and averages) over time.
How Can Sentiment Analysis be Used?
Sentiment Analysis finds a variety of applications within an organization to understand the voice of customers and employees. It plays a significant role for any business or organization. It helps data analysts within large enterprises gauge public opinion, conduct nuanced market research, monitor brand and product reputation, and understand customer experiences.
Let us look at two instances:
To understand the sentiments of consumers
Business analysts, customer service directors, including product and brand managers, can take advantage of sentiment analysis tools to understand what their customers are saying (or feeling) about their product or services.
Automated sentiment analysis tools can perform such functions faster and hence are preferred by professionals across industries.
Therefore, companies may use it for brand analysis, reputation crisis, campaign performances, and competitor analysis. Sentiment Analysis can also be used for analyzing the customer’s feedback of a specific company, regular users on social media towards a product, services, social issues, or political agendas.
Analyzing the customers’ sentiments helps the customer support team prioritize their work for offering better service to end-users.
To understand the sentiments of employees
In every business, Human Resource professionals face a significant problem, which is the retention of employees. Research shows that 20% of workers voluntarily leave their jobs each year, while another 17% are fired or let go. To combat this issue, companies are turning to data analytics to understand their employees’ voices.
With sentiment analysis, HR directors can analyze employee surveys, Slack messages, emails, and other communications to get the info they need to address pain points and improve morale proactively. In short, this gives them a deep understanding of what employees are discussing and how they feel.
What are the benefits of using effective Sentiment Analysis?
Using sentiment analysis, one can gauge how their customers feel about different business areas without reading thousands of customer comments at once.
The techniques and tools used by Textrics enable a company to drill down into different customer segments of the business and get a better understanding of sentiment in these segments.
With the help of our tool, market research processes can become much more straightforward. You can also improve customer satisfaction, discover new marketing strategies, improve media perceptions and crisis management, increase sales revenue, and so much more.
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