# Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
## 2 Related Work  
Public opinion analysis is becoming more important since opinions can be immediately expressed in social media. To understand public opinions in social media, the contents of user posts are usually analyzed. Words are the basic units of documents in natural language processing. It's useful to analyze the distributional relations of word occurrences in documents. Thus, word embedding models are usually used to map from the one-hot vector space to a continuous vector space with much lower dimension, such as Word2 Vec [1] and GloVe [2]. They both use neural networks to train the occurrence relations between words and documents in the contexts of training data. Specifically, [PERSON] et al. [14] tried to learn word vectors for sentiment analysis. Sentiment analysis techniques are often used to classify sentiment orientation of user opinions contained in social posts. They are useful in many applications such as analyzing user reviews. For example, [PERSON] et al. [15] conducted sentiment classification on movie reviews using machine learning methods. [PERSON] et al. [16] investigated spatial-temporal sentiment analysis of Tweets for US election. With the advances in deep learning methods, they have gradually shown better performance in sentiment classification. For example, convolutional neural networks (CNNs) have been shown to learn local features from words or phrases [17]. [PERSON] and [PERSON] [18] used deep CNNs for Twitter sentiment analysis at both message and phrase levels. On the other hand, recurrent neural networks (RNNs) allow the same hidden layers to process data in a temporal sequence. According to previous comparative study of RNN and CNN in natural language processing [19], RNNs are found to be more effective in sentiment analysis than CNNs. However, as the time sequence grows in RNNs, it's possible for weights to grow beyond control or to vanish. To deal with the vanishing gradient problem [20] in training conventional RNNs, long short-term memory (LSTM) [10] was proposed to learn long-term dependency during longer time period. For example, [PERSON] et al. [21] proposed to use attention-based LSTM for cross-lingual sentiment classification. [PERSON] and [PERSON] [22] proposed a feature ensemble model of surface and deep features for sentiment analysis of Arabic tweets. In this paper, we utilize LSTM in learning classifiers of sentiments in tweet contents. Stance detection is one of the research topics that are closely related to sentiment analysis. It aims at detecting if opinion holders are in favor or against a target. For example, [PERSON] et al. [23] proposed a joint neural network model to learn the stance and sentiment of a post at the same time. With shared representation of stance and sentiment information, their neural stacking framework with LSTM achieves the best performance on SemEval 2016 stance detection dataset [24] with F-measure of 63.54% and 74.89% for favor and against, respectively. [PERSON] et al. [25] combined feature representation for stance detection and fake news. For the FNC-1 dataset, they obtained an accuracy of 59.6%. Current research on stance detection assumes the availability of labels for user stances in a few selected topics in the dataset. For example, the Stance Detection dataset for Task 6: \"Detecting Stance in Tweets\" in SemEval 2016 [24] gives the corresponding labels of \"favor\", \"against\", and \"none of the above\" for about 2900 labeled training data instances in five different topics or targets, including: \"atheism\", \"climate change is a real concern\", \"feminist movement\", \"[PERSON]\", and \"legalization of abortion\". However, in this paper, we focus on extracting the aggregate stance of the public on a single topic and a set of comparable topics. Since there's no such corpus available, we have to construct our own dataset. In order to make the proposed method more applicable to real scenarios, we chose the election-related topics in Twitter. In addition to post contents, social media contain other features for sentiment analysis.