April 12, 2020
Sentiment analysis, also known as opinion mining, is the process by which companies or individuals can determine the tone of a text: is it positive, negative, or neutral? Grammar tools (often used by writers) use sentiment analysis engines and stylistic tools to tell you whether your writing is confident, optimistic or affirmative.
Thus, a sentiment analysis tool analyzes text by leveraging both machine learning and natural language processing (NLP) to identify specific tones or emotions in the text.
A variety of industries use sentiment analysis tools. They are infinitely useful for large entities in conducting market research, monitoring brand perception, and analyzing the overall customer experience through social media platforms and other media.
In this article, we will delve into the way sentiment analysis works, and analyze some of its primary use cases.
– Sentiment libraries
– Continuous improvement
– Rule-based sentiment analysis
Sentiment analysis follows a basic number of rules, as follows:
While this may seem complex, we instinctively do this all the time when we read.
Let’s take the following sentences as an example:
(a) I don’t like eating raw sushi.
(b) I absolutely loathe eating raw sushi.
In this context, it is clear to us that the second sentence (b) carries more weight and dislike compared to the first sentence. From this, we can tell that sentence (b) is much more negative.
For us, the process is simple, because we’ve read the phrase ‘don’t like’ and words ‘dislike’ and ‘loathe’ in different contexts across our lifetime, so we can compare them easily. A sentiment analysis tool follows a similar method.
Previous information and data fed into the tool help it identify which phrase/word is more negative/positive compared to other words and phrases. Thus, the more data entered into a sentiment analysis tool, the better it performs. Similarly to humans, the more you read or watch, or the more you hear different words (that carry a similar meaning) in different contexts, the more you can identify which word is more negative or positive compared to the other.
This brings us to the next section of this topic: sentiment libraries. As explained above, sentiment analysis tools will go back to its database to put a score to certain phrases/words (which we call ‘sentiments’).
A sentiment library, essentially, is somewhat of a dictionary or database for tools to go back to. It contains words/phrases of similar meanings:
Human coders add in this information and put a score for each word/phrase within their respective categories for the sentiment analysis tool to link sentiments accurately.
Every year, new words and phrases are added to the universal dictionary (for different languages). Thus, sentiment analysis tools need to be continually updated to meet the requirements of the current age. Addedly, there will be words that wouldn’t be added to the official dictionary, such as slang words often used across social media. Coders and analysts will have to add this information based on their research.
Also, the technology will always improve more when it receives increased user feedback. For example, with grammar tools that leverage sentiment analysis, it suggests that the text written is ‘confident’ and ‘assertive.’ Users can provide feedback to the engine by affirming this or saying that it isn’t correct. This type of feedback tremendously helps sentiment analysis tools improve almost automatically.
Overall, machine learning plays a central role in yielding accurate and relevant results. Read more about machine learning here: https://saal.ai/linguistic-feature-engineering-for-machine-learning/
There’s much more to the way sentiment analysis tools work. Engineers would have to add guidelines for the software to help it build rule-based relations between specific sentiments and specific words to score them accurately.
Also, the computer would need to understand grammar, and often each language has rules that it abides by (known as linguistic syntax), denoting and distinguishing ‘nouns’ from ‘adjectives.’ By feeding this information into the tool, it begins to understand the subsections of every sentence, and understand sentiment more accurately.
The use-cases for sentiment analysis are plenty. More dominantly, it is used among customer relations departments. It is also helpful for business analysts, marketing managers, human resource departments, and more stakeholders who need to input their customers’ responses and experience towards their products.
Essentially, this engine helps stakeholders and businesses understand their customers more closely, while taking out the painstakingly manual work that often comes as part of the package.
One negative comment can quickly get viral on social media, and it can wreak havoc for a company’s overall brand image. By making use of sentiment analysis, social media account managers can quickly manage negative comments by responding promptly and addressing customer complaints before it reflects adversely on the company. This is especially useful for large enterprises whose community management efforts need computer support.
Sentiment analysis can yield more followers and offer an improved customer experience online that can generate results. This includes increased sales and an improved perception of the brand outside of its online presence. Business analysts can analyze how their customers feel about them online. This would immensely help in future marketing and PR campaigns, and can be a valuable starting point for crucial future business decisions.
Rather than listening to each call manually to analyze the performance of customer service representatives, a sentiment analysis tool that is powered by voice recognition can automate this and offer valuable results to managers and employees. It can provide insights into which phrases work and yield the most positive feedback from customers and calls. It can also suggest phrases and tactics to avoid.
Gathering insights can go beyond just on social media. Sentiment analysis engines can gather information from across the internet, such as through product reviews, articles, and blogs. When collected, this information can help analysts enhance their brand reputation by understanding how the world is perceiving them and talking about them.
Sentiment analysis engines are powerful tools in today’s world. They can be used to predict future voting results as it analyzes peoples’ sentiments, and it can help guide significant business decisions with valuable real-time data.
Saal, an artificial intelligence company based in UAE, offers a sentiment analysis tool for companies and teams to increase customer satisfaction, brand perception, and sales.
Discover more AI-enabled tools from Saal here: https://saal.ai/cognitive-cloud/ai-modules/
Here’s an overview of the sentiment analysis engine provided by Saal: