14 Key Insights An Advanced Sentiment Analysis Software Shares

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A sentiment analysis software uses AI and machine learning to analyze text, audio, and video conversations to identify the tone, intent, and emotion behind every message. While a regular sentiment analysis platform will give the aggregate score of the emotion around a product or a brand, an advanced sentiment analysis solution offers much deeper insights that have a genuine effect on your action plan for growth. In this article, we check out the 14 key insights that such a sentiment analysis software shares.

What Is Sentiment Analysis?

Sentiment analysis is the method of understanding feelings expressed in data through machine learning tasks such as text analysis, video content analysis, natural language processing, semantic clustering, and others. A sentiment analysis software gives organizations data-driven insights to understand current market trends, predict new consumer trends, gaps in customer engagement, product and service improvement ideas, as well as insights to enhance employee experience.

What Are The Advanced Features Of A Sentiment Analysis Platform?

A sentiment analysis dashboard can give a business very granular insights from the data it processes. To do so, it uses several of its advanced features, such as those below:

1. Ability to choose industry-based aspects (& create customer aspect)

The accuracy of the insights you get from any machine learning platform comes from its named entity recognition (NER) capability. NER makes sure that every entity (person, organization, logo, place, currency, etc.) that is relevant to your industry is extracted for semantic and sentiment analysis.

Each industry has its own aspects, such as a restaurant will have its own aspects (food, drinks, ambiance, price, customer service, etc), while a bank will have aspects pertaining to its industry (salary, money, deposit, withdrawal, etc.). An advanced sentiment analysis platform will not only allow you to choose industry aspects based on your industry but also custom-create aspects that are niche and unique to you.

2. Ability to evaluate multiple social media sources

An advanced sentiment analysis software will allow you to collect and analyze data from a variety of sources, both text-based as well as video-centric. This includes social media platforms as well as other online platforms for product reviews. For example, the Repustate sentiment analysis platform, Repustate IQ, is capable of analyzing data from such sources as TikTok, Douyin, Amazon Reviews, Instagram, Facebook, Twitter, and others.

3. Ability to analyze video content

In order to gain a thorough understanding of your customer base and the market perception of your product and brand, you need to gather as much information as possible. The more varied the data sources the better.

In this regard, it is very important that the sentiment analysis software is able to extract findings from video content as well. Video AI allows you to get insights from YouTube video analysis or obtain TikTok insights as easily as you would get from any text-based source such as Twitter, TrustPilot, or a news website.

What Insights Does A Sentiment Analysis Software Share?

A sentiment analysis platform gives you insights based on sentiment by language, data source, and ofcourse, the general sentiment overview. You can analyze entities, see their frequency, get granular aspect-based sentiment, and see the change in sentiment over time. There are several other key insights you can get. We discuss them all in detail below:

1. General sentiment overview & sentiment score

One of the most basic, yet principle things you can see in the insights generated by a sentiment analysis API is the general sentiment overview of the subject you are analyzing. The sentiment analysis software will crawl the internet for all relevant data or will analyze a data sheet that you have manually uploaded, and give you the number of total documents it has analyzed and the sentiments in color codes. In this case, green for positive, red for negative, and blue for neutral.

2. Aspect-based sentiment breakdown

A very important result that the sentiment analysis platform gives is a fine-grained analysis of every single aspect it discovers and categorizes from the data. The only true way a business can successfully remain relevant and thrive in a tough competitive environment is when it reads the room, in this case, the voice of its customers.

Had shoe company Aldo listened to what its customers were saying about the tawdry quality of their shoes and bags, as well the customer service, they would not be facing the situation they are in today. In contrast, 80-year-old business Browns Shoes remains strong as ever as it continues to evolve by listening to its customers and understanding market trends.

Aspect-based sentiment analysis gives you this power to carve your own niche by keeping an eye on every single detail about your brand.

3. Aspect occurrence frequency rates

Another interesting thing the sentiment analysis platform shows you is how frequently an aspect appears in the data. This gives you an idea of which aspect of your product, service, or overall brand, is being talked about the most. See image below.

4. Named entities, classifications, and entity frequency

The sentiment analysis software applies named entity recognition and gives you entities that it has discovered, extracted, and analyzed. For example, it will tell you that “love” is an emotion a person has, or that French is the nationality of a person (see image below).

It is interesting to note, that as it classifies these entities, it realizes the difference between “French” the language, and “French” the people. This is due to its capability to derive intelligence from semantics and contextual reference.

This is a vital advantage that Repustate IQ has over other similar emotion mining and analysis platforms.

5. Sentiment by keyword

You can also get sentiment by keyword. The sentiment analysis platform processes all the data related to customer experience, healthcare, the voice of the employee, brand monitoring, and such, and immediately picks out the sentiment attached to important words, as in, the most commonly occurring words and phrases, in that project.

This is useful because you get a very holistic picture of the data you are analyzing and even pick up things that didn’t even occur to you as a brand.

Also read: NLP Examples

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