What are the Main Steps of Sentiment Analysis?
Every word we write has some emotion. We might be expressing sorrow or hate through our written comments. You have to interpret these phrases to find the hidden meaning. For this, we take advantage of sentiment analysis. It is called opinion mining or emotion analysis, which helps decode emotions in the text through a series of steps. You can analyze feedback, social media comments, or product reviews. It all depends on how the system perceives sentences in a text. You can integrate several algorithms and libraries to perform quick analysis.
The following lines explain what are the main steps in carrying out the sentiment analysis project. And how various businesses can use this opinion mining method to their benefit. Once you understand the process, you can easily use it to your benefit.
Understanding the Phenomenon of Opinion Mining
Sentiment analysis helps find what emotions a person is experiencing at the current time. It could be positive, negative, or neutral. Most of the time, e-commerce industries use such analysis methods to find customer sentiment. It helps them keep track of their success rate and market reputation. For example, if you put up a new commercial or launch a new product, you can use a sentiment analysis tool to distinguish if it’s a success or not.
However, to decode emotions, we must process natural human language using algorithms and NLP. You can use these findings to formulate successful business plans.
Main Steps in the Sentiment Analysis Process
Before going ahead, we must consider what are the main steps in carrying out sentiment analysis projects. Well, it’s a multi-step process. You train data, analyze it, and get relevant results. The process is as follows:
The most crucial step in the entire process is collecting relevant data. If you are an e-commerce business, you can collect reviews or customer comments from your social media pages and analyze them to find your market position. The better the data quality, the greater the accuracy of the results. You can collect data manually or through live APIs.
After collecting data, you must prepare it. This step is called data processing. Your data will comprise videos, images, and even audio clips. These will be converted into one form. The tasks covered in this step include:
- Caption overlay
- Audio transcription
- Logo recognition
- Image overlay
The third step in the analysis process is analyzing data. We have to train the model so it can classify and label the incoming data. Once the system parses every phrase, it scores it according to the polarity and subjectivity of the text. These sentiment scores determine if a piece of text has negative or positive emotions.
It is the last part of the analysis mechanism. Here your results will be displayed in the form of graphs and charts. Visual representation helps you understand your weaknesses.
Popular Approaches to Sentiment Analysis
After explaining the main steps of sentiment analysis, we move to their work. As told earlier, our process includes natural language processing and various machine learning mechanisms. These two systems help us perform analysis in two ways:
The rule-based analysis uses a set of traditional rules. It involves NLP techniques, like tokenization, stemming, and parsing. The system starts by creating lexicons. These are lists of negative and positive words describing various emotions. After the creation of lexicons, data is in a computer-understandable form. Now, it will perform the scoring process. Positive and negative emotions are counted, and an overall score is calculated.
Even though the process is well known, there are a few limitations. The method doesn’t consider the entire sentence and can sometimes ignore negations and metaphors. Moreover, you might need to update the system to enhance its performance.
Automated or ML Analysis
Other than rule-based analysis, you can also use automated systems. These rely on machine learning algorithms. The algorithm classifies phrases according to the order they are arranged in. You will get a better result when the training is more accurate. The process starts with feature extraction and is followed by training and prediction to help the algorithm perform well on the incoming data. Once the training is complete, the model gives precise results.
Aspect-Based Sentiment Analysis
Another analysis technique used in today’s time is aspect-based sentiment analysis. It analyzes data depending on its aspects and attributes positive or negative sentiments to it. From tweets to customer reviews or even movie reviews, you can use it for any dataset. This sentiment analyzer detects neutral sentiments accurately.
ML Algorithms Used for Opinion Mining
Several algorithms help us to find emotions within a piece of text. The following algorithms are more efficient and popular.
- Naïve Bayes: Here you use probabilistic algorithms. A probability is calculated, and the text is labeled accordingly.
- Logistic Formulas: When one needs results in binary form, one should use logistic regression. It uses a sigmoid function, which gives a score between 0 and 1.
- Linear Regression: We use linear regression to predict the polarity of phrases. It accurately predicts sentiments.
Before analyzing any text, you must know about its sentiment score. Every intelligent machine gives a sentiment score to the data it receives. Sentence polarity is determined depending on the word’s intensity. It can be negative or positive. This polarity is represented in a mathematical form called the sentiment score. The system will calculate sentiment scores individually and then collectively. The average value determines the polarity of the text.
Real-World Uses of Opinion Mining
The process comes in handy in multiple fields, especially in the business sector. You can make sense of various forms of data and use them to expand your work. Some business fields where sentiment analysis plays a crucial role are:
- Product experience: Sentiment analysis can also identify whether customers like a new product or not. Analyzing online product reviews help you face market competition.
- Brand analysis: Through the opinion mining process, companies can analyze how people perceive their brand image. If the response is positive, you will see growth in your sales.
- Social media analysis: You can encourage your clients to drop honest opinions on your social media handles or tweet about your products. Using these comments, you can find your strengths and weaknesses.
Sentiment Analysis Solutions according to Modern Day Needs
The language of today’s generation has evolved to an extent where emojis, emoticons, and humor are present in every sentence. For computers, understanding variations in emotions can be confusing. Thus, we have to use a platform that caters to every element. Using machine learning algorithms and various libraries, we can achieve our goal and get an accurate analysis report.
Challenges Faced in the Process
Even though finding emotions in real-time can be beneficial, it is tough. Many elements might affect the accuracy of results. One thing is sarcasm which is difficult to detect in text. It gives false results. Similarly, negations and intensifiers cause problems. Before using any analysis model, you must cater to these issues.
Using Opinion Mining to Succeed in the Market
Opinion mining has numerous benefits. The most significant one is finding customer emotions. It can directly impact your business success and provide you with correct insight. You can analyze any feedback and see which problems you need to fix. The process is lengthy and comprises multiple steps, like data collection, processing, and analysis. However, the use of NLP and machine learning algorithms make your journey easier. All that you need to do is pick the correct model and train it vigorously. Once you go through each step, the analysis will be authentic. You can use it on any data type and accurately predict the hidden sentiments.
To grow your business, you need strategies and advanced systems. Opinion mining mechanisms help you gather important information about how people perceive your brand and its products. Therefore, you must use such systems for business expansion. You can also take help from companies like VizRefra, which provides programs and services to promote your work. We offer every service, including sentiment analysis services. These programs help you achieve your business expansion.