Add to ORKGWe study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different datasets and compare its performance with the current state-of-the-art techniques for emotion detection, including a recursive auto-encoder. The results of the experimental study suggest that RBEM-Emo is a promising approach advancing the current state-of-the-art in emotion detection
Affective computing is the study and development of devices that can recognize emotions through vari...
Event detection helps people to identify 'meaningful' events from documents. The most common form of...
Abstract — In this paper, we use machine learning techniques to try to find the best possible soluti...
We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wh...
Sentiment analysis can go beyond the typical granularity of polarity that assumes each text to be po...
Abstract Opinion mining techniques, investigating if text is expressing a positive or negative opin...
Social media and microblog tools are increasingly used by individuals to express their feelings and ...
We propose the Rule-Based Emission Model (RBEM) algorithm for polarity detection. RBEM uses several ...
This paper provides an overview of the evolving field of emotion detection and identifies the curren...
People\u27s emotions can be gleaned from their text using machine learning techniques to build model...
The explosion of social media services presents a great op-portunity to understand the sentiment of ...
We propose the Rule-Based Emission Model (RBEM) al-gorithm for polarity detection. RBEM uses several...
With the proliferation of social media, textual emotion analysis is becoming increasingly important....
Affective computing is the study and development of devices that can recognize emotions through vari...
Event detection helps people to identify 'meaningful' events from documents. The most common form of...
Abstract — In this paper, we use machine learning techniques to try to find the best possible soluti...
We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wh...
Sentiment analysis can go beyond the typical granularity of polarity that assumes each text to be po...
Abstract Opinion mining techniques, investigating if text is expressing a positive or negative opin...
Social media and microblog tools are increasingly used by individuals to express their feelings and ...
We propose the Rule-Based Emission Model (RBEM) algorithm for polarity detection. RBEM uses several ...
This paper provides an overview of the evolving field of emotion detection and identifies the curren...
People\u27s emotions can be gleaned from their text using machine learning techniques to build model...
The explosion of social media services presents a great op-portunity to understand the sentiment of ...
We propose the Rule-Based Emission Model (RBEM) al-gorithm for polarity detection. RBEM uses several...
With the proliferation of social media, textual emotion analysis is becoming increasingly important....
Affective computing is the study and development of devices that can recognize emotions through vari...
Event detection helps people to identify 'meaningful' events from documents. The most common form of...
Abstract — In this paper, we use machine learning techniques to try to find the best possible soluti...