Sentiment Analysis

Usage Example

Consider PC reviews on a website such as Amazon. In this case, the product seller would not only like to know about the overall review but also specific reviews such as battery life, sound quality, storage space, etc. This will also help prospective buyers to know in-depth about product specificities. In such a case, we need to extract the topic-specific user reviews. These topics are known as ‘entities’ or ‘aspects’. So, given a review, we first try to identify the aspects or entities and then classify the aspect-specific review as positive, negative, or neutral.

Our Sentiment Analysis Models

We provide the following three models for sentiment analysis which differ from each other based on the type of word vectorization and model classifiers used.

  1. Our model to evaluate sentiments of English language news is based on TF-IDF vectorization of text. To deal with ambitious word combinations like “not terrible”, “not bad” we applied n-grams splitting of the text. C-Support Vector Classifier was used at the top of it. To train our model we used a human-labeled dataset of news titles sentiment created by Connexun team.
  2. Multilingual sentiment model based on the pre-trained joeddav/xlm-roberta-large-xnli model. This model is a result of fine-tuning of xlm-roberta-large on a combination of Natural Language Inference (NLI) data in many languages. It is widely used for zero-shot text classification. NLI approach defines is two sentences in entailment between each other or in contradiction or neutral with respect to each other.
  3. Sentiment analysis of entities with respect to their context exploits the Aspect Based Sentiment Analysis. On top of this model, we fine-tuned the logistic regression classifier which provides probabilities that a given NER has positive or negative sentiment in a given sentence. The logistic regression was trained on a human-labeled training dataset created in Connexun.

Application in various industries. Recommender systems for e-commerce

Bank Performance and News Sentiment

The movement in stock prices of a firm depends on its overall market sentiments. Thus, people who perform stock trading also need to know the news sentiment of that particular firm in the market at that point in time. This can be done by monitoring public opinions about the company performance or the company stock on a real-time basis. News articles and social media sites such as Twitter and Facebook can be a very good source of monitoring public views through tweets. A dataset of the firm-related sentiments can be created on a periodic basis which can be used to find the daily average news sentiment of the company. We can plot these values against the closing index prices to identify certain trends in sentiments that can be used to predict stock prices with precision.

Our Services

Connexun offers a range of API services for its users that perform varied tasks. Our Text Analysis APIs performs text summarization, language detection, sentiment evaluation, etc. Our Sentiment Evaluation API takes in as input a paragraph of 15 to 500 words and assigns an overall sentiment-positive, negative or neutral to it and sentiment score which is a finite floating point number telling us the confidence score for the sentiment label.

Conclusion

Market sentiment is a powerful tool, which when harnessed in a timely manner, can help turn tides for both new & existing products alike. So now you know the what, why & how of entity-based sentiment analysis. We hope you’ll be able to use our APIs to the fullest to gain the edge over competitors by roping in the public sentiments

About Connexun

Connexun is an innovative tech startup based in Milano, Italy.

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Connexun | news api

Connexun | news api

Connexun is the ultimate AI news engine — turning unstructured news content into multi-purpose actionable data.