Question: What type of lexical, syntactic, semantic and polarity features are used?
Evidence: Our lexical features include 1-, 2-, and 3-grams in both word and character levels. For each type of INLINEFORM0 -grams, we utilize only the top 1,000 INLINEFORM1 -grams based on the term frequency-inverse document frequency (tf-idf) values. That is, each INLINEFORM2 -gram appearing in a tweet becomes an entry in the feature vector with the corresponding feature value tf-idf. We also use the number of characters and the number of words as features.
We use the NLTK toolkit to tokenize and annotate part-of-speech tags (POS tags) for all tweets in the dataset. We then use all the POS tags with their corresponding tf-idf values as our syntactic features and feature values, respectively.
Firstly, we employ 300-dimensional pre-trained word embeddings from GloVe BIBREF29 to compute a tweet embedding as the average of the embeddings of words in the tweet.
Secondly, we apply the latent semantic indexing BIBREF30 to capture the underlying semantics of the dataset. Here, each tweet is represented as a vector of 100 dimensions.
Thirdly, we also extract tweet representation by applying the Brown clustering algorithm BIBREF31 , BIBREF32 —a hierarchical clustering algorithm which groups the words with similar meaning and syntactical function together. Applying the Brown clustering algorithm, we obtain a set of clusters, where each word belongs to only one cluster. For example in Table TABREF13 , words that indicate the members of a family (e.g., “mum”, “dad”) or positive sentiment (e.g., “interesting”, “awesome”) are grouped into the same cluster. We run the algorithm with different number of clustering settings (i.e., 80, 100, 120) to capture multiple semantic and syntactic aspects. For each clustering setting, we use the number of tweet words in each cluster as a feature. After that, for each tweet, we concatenate the features from all the clustering settings to form a cluster-based tweet embedding.
Motivated by the verbal irony by means of polarity contrast, such as “I really love this year's summer; weeks and weeks of awful weather”, we use the number of polarity signals appearing in a tweet as the polarity features. The signals include positive words (e.g., love), negative words (e.g., awful), positive emoji icon and negative emoji icon. We use the sentiment dictionaries provided by BIBREF33 to identify positive and negative words in a tweet. We further use boolean features that check whether or not a negation word is in a tweet (e.g., not, n't).
Our lexical features include 1-, 2-, and 3-grams in both word and character levels.
We also use the number of characters and the number of words as features.
We then use all the POS tags with their corresponding tf-idf values as our syntactic features and feature values, respectively.
Firstly, we employ 300-dimensional pre-trained word embeddings from GloVe BIBREF29 to compute a tweet embedding as the average of the embeddings of words in the tweet.
Secondly, we apply the latent semantic indexing BIBREF30 to capture the underlying semantics of the dataset. Here, each tweet is represented as a vector of 100 dimensions.
Thirdly, we also extract tweet representation by applying the Brown clustering algorithm BIBREF31 , BIBREF32 —a hierarchical clustering algorithm which groups the words with similar meaning and syntactical function together.
Motivated by the verbal irony by means of polarity contrast, such as “I really love this year's summer; weeks and weeks of awful weather”, we use the number of polarity signals appearing in a tweet as the polarity features. The signals include positive words (e.g., love), negative words (e.g., awful), positive emoji icon and negative emoji icon. We use the sentiment dictionaries provided by BIBREF33 to identify positive and negative words in a tweet. We further use boolean features that check whether or not a negation word is in a tweet (e.g., not, n't).
Answer: The answers are shown as follows:
* Our lexical features include 1-, 2-, and 3-grams in both word and character levels.
* number of characters and the number of words
* POS tags
* 300-dimensional pre-trained word embeddings from GloVe
* latent semantic indexing
* tweet representation by applying the Brown clustering algorithm
* positive words (e.g., love), negative words (e.g., awful), positive emoji icon and negative emoji icon
* boolean features that check whether or not a negation word is in a tweet