After months of waiting and postponing, your friend finally agrees to watch a movie of your choice, and after it ends, he says, "That was the best movie ever. It was just awesome, so much that within the first twenty minutes, I dozed off, and by the intermission, I was snoring out loud." The inherent irony in the review is visible in your friend's dissatisfaction, and without giving it a second's thought, you could understand what he meant.
Now, consider a similar situation befalling your business. There's no end to the amount of data you receive from numerous sources, at varying paces, and indifferent volumes. Most of this data from emails, candidate resumes, call and meeting recordings, social media reviews, customer feedback, etc., are unstructured. And the outrageous volume makes it challenging to analyze all the data manually to understand what it implies. Consider how misunderstanding data can be detrimental and damaging to your company's reputation and overall success if they are not identified and addressed.
One of the most reliable solutions to avoid such circumstances is implementing sentiment analysis, especially for understanding and deriving context and insights from unstructured data.
Let's understand the nitty-gritty of sentiment analysis by answering some pertinent questions!
What is Sentiment Analysis?
Sentiment analysis empowers businesses to comprehend emotions and psychology behind any data point. Identifying positive or negative emotions in the text falls under the basic function of sentiment analysis. In essence, the process includes analyzing feelings (opinions, thoughts, attitudes) behind words by using Natural Language Processing (NLP) tools. Businesses employ sentiment analysis to assess brand reputation, identify emotions and attitudes in social data and better understand their clients, users, and customers.
If you’re analyzing the sentiment, sentiment score is one value that is needed for the analysis. Sentiment score is a numerical measure that reflects the emotional depth of emotions in a text. It detects and rates client emotions from 0 (most negative) to 10 (most favorable). Customer happiness and text categorization are improved by sentiment scores.
Take, for example, analyzing the minutes of a critical company meeting or tracking company news from across the world. Sentiment analysis is a type of text analytics that can help data workers assess the feelings behind anything and everything shared in the form of unstructured data formats collected from different sources. The process begins with analyzing a piece of text (a line, a comment, or the complete document), and giving it a score to show whether it is positive, negative, or neutral. It gives you access to the correct data at the right moment, making it a game-changer for making business-critical decisions.
Why is Sentiment Analysis important?
Since data is now gathered from every corner in formats more than ever before, sentiment analysis becomes an essential tool to track company or client news, global trends, analyze documents and reports, etc. Some of the reasons that make sentiment analysis so critical for businesses today include:
- Improving customer service leads to a better customer experience
- Boosting your products and services
- Improve overall performance
- Monitoring brand perceptions
- Tracking sentiments in real-time
- Sorting data at scale
- Maintaining consistent criteria for a centralized sentimental analysis
- Preventing insights vacuum
- Automating and streamlining data extraction, thereby saving time and efforts
- Improving scalability and flexibility
How Does Sentiment Analysis Work?
When it comes to assessing the emotional tone of online conversations, NLP and machine learning tools are put to use. You can employ a variety of methods in sentiment analysis models based on the amount of data you need to analyze and the accuracy you demand. Sentiment analysis algorithms are of three types:
These systems employ manually defined rules to perform sentiment analysis automatically.
Under this approach, the major dependence is on machine learning algorithms to gain insights from the sea of data available.
These systems mix the two approaches mentioned above.
Conducting sentiment analysis is quite a straightforward process. It includes:
- Dissecting text documents into their different elements.
- Recognizing all the sentiment-bearing terms and components to evaluate them.
- Allocating a sentiment score to each of the dissected phrases and components.
- Combining the scores to conduct a multi-layered sentiment analysis.
What are the Challenges Knowledge Workers Face in Sentiment Analysis?
Since it is challenging to interpret sentiments, sentiment analysis becomes one of the most difficult tasks in natural language processing. Although data scientists are improving their ability to create more accurate sentiment classifiers, they still have a long way to go. Following are some of the primary issues pertaining to sentiment analysis:
Subjectivity & Tone
Texts are either objective or subjective. While the former does not include explicit emotions, the latter does, and it becomes difficult to understand them based on some predicates.
Context & Polarity
The same word or phrase can express two entirely different emotions based on the time, place, and situation it was expressed at, making it a challenge for NLP tools to assess.
Irony & Sarcasm
Expressing negative emotions using positive words makes it hard to comprehend the actual meaning.
Understanding the meaning of texts in the socially active world becomes difficult as emojis form a major part of the data.
Use Cases of Sentiment Analysis
Sentiment analysis has a broad range of applications, including e-commerce, marketing, advertising, politics, and market research. Even though the applications for sentiment analysis are interrelated, they all aim to improve performance via study of changes in public opinion.
Let's take a deeper look at how sentiment analysis may help these areas.
Social Media Monitoring:
Sentiment analysis can help companies figure out how people feel about their brand, goods, or services by looking at posts, comments, and mentions on social media. This lets companies find possible problems, keep track of their brand's image, and take proactive steps to deal with customer complaints.
Sentiment analysis may help market researchers analyze large volumes of text data from consumer surveys, earnings call transcripts, and internet forums. It reveals real-time trends, sentiment changes, and emerging issues, helping firms optimize their plans, find new market possibilities, or create focused marketing campaigns.
Needl.ai centralizes data sources and streams updates to simplify sentiment analysis for market researchers. Needl.ai auto-organizes and prioritizes signal amplification to help market researchers enhance strategy, discover market possibilities, and optimize marketing efforts.
Brand Reputation management:
Sentiment analysis can help businesses keep an eye on their online image by looking at how people feel about them in news stories, blog posts, or online conversations. This lets companies find and fix any bad feelings quickly, handle problems well, and keep a good brand image.
Sentiment analysis can be used to analyze customer feedback collected from surveys, reviews, and customer support interactions. It helps identify positive and negative sentiment patterns, enabling businesses to understand customer satisfaction levels, uncover areas for improvement, and enhance the overall customer experience.
By analyzing sentiment patterns and detecting negative sentiment spikes, businesses can quickly identify potential issues, address them promptly, and minimize reputational damage. For example, During a crisis or public relations incident, needl.ai can be used to monitor social media and news sentiment in real-time.
If you wish to discover valuable information from a data haystack, Needl.ai offers a wide range of data processing and synthesis capabilities, both machine, and human-driven. Summarization, sentiment analysis, transcription, conversion from one format to another, and other tasks will be automated using machine-driven technologies. As a result, users effortlessly integrate typical data processing procedures, which necessitate switching between various technologies, applications, or even human resources.
In addition to conducting sentiment analysis, we also provide several human-driven data synthesis tools that will allow users to clip, highlight, and take notes across various data sources, then synthesize them into a personal summary record for retrieval and augmentation later on.
Book a Demo now with Needl.ai