PDF Natural Language Processing for Sentiment Analysis: An Exploratory Analysis on Tweets

The human brain cannot track billions of parameters, and we still do not know how to apply mathematics to interpret them reliably. If interpretability is an issue for you, you should stick to the classical sentiment analysis model. Deep learning models have gained significant popularity in the field of sentiment analysis. Neural networks are trying to mimic the human brain with billions of neurons and synapses, making their ability to capture complex patterns in large-scale datasets undisputable. You can use sentiment analysis to understand how customers perceive your product, brand, and company. By analyzing customer feedback, you can get invaluable insights that shape your strategies for brand management, reputation management, and customer experience.

• GPU-accelerated DL frameworks offer flexibility to design and train custom deep neural networks and provide interfaces to commonly-used programming languages such as Python and C/C++.
• Sentimental models are generally classified by polarity, urgency, emotionality, and intentions.
• Google Natural Language processing API is a pre-trained machine learning API that gives developers access to human-computer interaction, Google sentiment analysis, entity recognition, and syntax analysis.
• We have successfully trained and tested the Multinomial Naïve Bayes algorithm on the data set, which can now predict the sentiment of a statement from financial news with 80 per cent accuracy.

Sentiment analysis is an incredibly valuable technology for businesses because it allows getting realistic feedback from your customers in an unbiased (or less biassed) way. Done right, it can be a great value-added to your systems, apps, or web projects. See what happens when custom Kindle trained data meets IMDB data.Additionally, a lot of reviews went to the neutral sack showing a bad situation  positive-negative separation. Social media users are able to comment on Twitter, Facebook and Instagram at a rate that renders manual analysis cost-prohibitive.

Next Steps

We will pass this as a parameter to GridSearchCV to train our random forest classifier model using all possible combinations of these parameters to find the best model. Now, we will convert the text data into vectors, by fitting and transforming the corpus that we have created. Because, without converting to lowercase, it will cause an issue when we will create vectors of these words, as two different vectors will be created for the same word which we don’t want to. WordNetLemmatizer – used to convert different forms of words into a single item but still keeping the context intact. Now, as we said we will be creating a Sentiment Analysis Model, but it’s easier said than done.

Its applications span multiple sectors, aiding customer feedback analysis, shaping political campaigns, and enhancing digital interactions. However, challenges such as interpreting sarcasm and irony, addressing data privacy concerns, and adapting to multilingual contexts remain significant hurdles. Despite these challenges, sentiment analysis continues to evolve, offering more profound insights into human emotions and communication.

How to Do Customer Sentiment Analysis

Possessing such information and implementing machine learning algorithms, can increase customer loyalty to your company. After all, it is important for everyone to be heard and understand the personal attention to the service. The model reveals such aspects of emotions as sadness, joy, anger, disappointment, sadness, happiness, etc. With the help of machine learning algorithms, it’s possible to hide the inaccuracy and ambiguity of the natural lexicon.

Then you could dig deeper into your qualitative data to see why sentiment is falling or rising. Emotion detection sentiment analysis allows you to go beyond polarity to detect emotions, like happiness, frustration, anger, and sadness. There are many packages available in python which use different methods to do sentiment analysis.

What you are left with is an accurate assessment of everything customers have written, rather than a simple tabulation of stars. This analysis can point you towards friction points much more accurately and in much more detail. It’s estimated that people only agree around 60-65% of the time when determining the sentiment of a particular text.

Read more about Sentiment Analysis NLP here.