What is Sentiment Analysis?
Sentiment analysis is the process of extracting a general sentiment from a block of text. Basically it's about determining whether the text is positive or negative.
For example, let's imagine our program finds the following Twit:
Look what's just come on the market in #ValThorens! A recently renovated, charming 6 bed duplex apartment in the heart of the resort with superb views!
This is a commercial Twit that clearly shows a positive sentiment.
The NLP model in charge of sentiment analysis would return the main sentiment and its likelihood. Here we would get a positive sentiment with a high likelihood.
Why Use Sentiment Analysis?
Sentiment analysis can be interesting in many situations. Let's give you a couple of examples.
Social Network Analysis
Imagine you're working in a marketing department that is regularly posting new content on social networks. You might want to automatically monitor the user reactions in order to quickly intervene in case of negative feedback.
Some support requests might be more urgent than others, depending on how angry users are. Detecting the user's sentiment automatically can help support address critical tickets more quickly.
Gauging the sentiment of a couple of persons on the internet is easy, but understanding the global sentiment of thousands of persons is another thing. Automated sentiment analysis is the key solution here.
Right after launching a new product, it can be critical to react quickly in case of poor reception by customers, bloggers, journalists... Sentiment analysis can help in such situations.
AI models don't always work well with non-English languages.
We do our best to add non-English models when it's possible. See for example Fine-tuned LLaMA 2 70B, Dolphin, ChatDolphin, XLM Roberta Large XNLI, Paraphrase Multilingual Mpnet Base V2, or spaCy. Unfortunately not all the models are good at handling non-English languages.
In order to solve this challenge, we developed a multilingual module that automatically translates your input into English, performs the actual NLP operation, and then translates the result back to your original language. It makes your requests a bit slower but often returns very good results.
Even for models that natively understand non-English languages, they actually sometimes work even better with the multilingual addon.
Simply select your language in the list, and from now on you can write the input text in your own language!
This multilingual add-on is a free feature.