Add to ORKGIn event detection and opinion mining on social media it is important to grasp the semantic meaning of a text post. In this abstract, we present a method to learn effective representations for Twitter posts through a combination of word embeddings and word frequency information. We design a semantic similarity task between tweet couples and a novel loss function to train our model. We test it on a manually crafted dataset of tweets, and we find that our method outperforms the traditional baselines
Event detection from social media messages is conventionally based on clustering the message content...
Abstract There is a growing interest in using social media content for Natural Language Processing a...
Twitter has become the largest microblogging platform where users can interact between each other e...
In event detection and opinion mining on social media it is important to grasp the semantic meaning ...
In event detection and opinion mining on social media it is important to grasp the semantic meaning ...
It has been shown that learning distributed word representations is highly useful for Twitter sentim...
A word embedding is a low-dimensional, dense and real-valued vector representation of a word. Word e...
Social media sites are one of the platforms where a lot of people interact in the present, expanding...
In this paper, we develop a deep learn-ing system for message-level Twitter sen-timent classificatio...
Researchers on social-media understandably assert that the contributions social media has made on va...
Researchers on social-media understandably assert that the contributions social media has made on va...
Researchers on social-media understandably assert that the contributions social media has made on va...
Scholars often seek to understand topics discussed on Twitter using topic modelling approaches. Seve...
There is a growing interest in using social media content for Natural Language Processing applicatio...
In this paper, we propose a regression system to infer the emotion intensity of a tweet. We develop ...
Event detection from social media messages is conventionally based on clustering the message content...
Abstract There is a growing interest in using social media content for Natural Language Processing a...
Twitter has become the largest microblogging platform where users can interact between each other e...
In event detection and opinion mining on social media it is important to grasp the semantic meaning ...
In event detection and opinion mining on social media it is important to grasp the semantic meaning ...
It has been shown that learning distributed word representations is highly useful for Twitter sentim...
A word embedding is a low-dimensional, dense and real-valued vector representation of a word. Word e...
Social media sites are one of the platforms where a lot of people interact in the present, expanding...
In this paper, we develop a deep learn-ing system for message-level Twitter sen-timent classificatio...
Researchers on social-media understandably assert that the contributions social media has made on va...
Researchers on social-media understandably assert that the contributions social media has made on va...
Researchers on social-media understandably assert that the contributions social media has made on va...
Scholars often seek to understand topics discussed on Twitter using topic modelling approaches. Seve...
There is a growing interest in using social media content for Natural Language Processing applicatio...
In this paper, we propose a regression system to infer the emotion intensity of a tweet. We develop ...
Event detection from social media messages is conventionally based on clustering the message content...
Abstract There is a growing interest in using social media content for Natural Language Processing a...
Twitter has become the largest microblogging platform where users can interact between each other e...