Question: What does the author do in order to make English sentiment analysis as general as possible?
Evidence: In order to eliminate the need to find data and build separate models for each language, we propose a multilingual approach where a single model is built in the language where the largest resources are available. In this paper we focus on English as there are several sentiment analysis datasets in English. To make the English sentiment analysis model as generalizable as possible, we first start by training with a large dataset that has product reviews for different categories. Then, using the trained weights from the larger generic dataset, we make the model more specialized for a specific domain. We further train the model with domain-specific English reviews and use this trained model to score reviews that share the same domain from different languages. To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place. Figure FIGREF1 shows our multilingual sentiment analysis approach. It is important to note that this approach does not utilize any resource in any of the languages of the test sets (e.g., word embeddings, lexicons, training set).
Deep learning approaches have been successful in many applications ranging from computer vision to natural language processing BIBREF20 . Recurrent neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are subsets of deep learning algorithms where the dependencies between tokens can be used by the model. These models can also be used with variable length input vectors which makes them suitable for text input. LSTM and GRU models allow operations of sequences of vectors over time and have the capability to `remember' previous information BIBREF20 . RNN have been found useful for several natural language processing tasks including language modeling, text classification, machine translation. RNN can also utilize pre-trained word embeddings (numeric vector representations of words trained on unlabeled data) without requiring hand-crafted features. Therefore in this paper, we employ an RNN architecture that takes text and pre-trained word embeddings as inputs and generates a classification result. Word embeddings represent words as numeric vectors and capture semantic information. They are trained in an unsupervised fashion making it useful for our task.
The sentiment analysis model that is trained on English reviews has two bidirectional layers, each with 40 neurons and a dropout BIBREF21 of 0.2 is used. The training phase takes pre-trained word embeddings and reviews in textual format, then predicts the polarity of the reviews. For this study, an embedding length of 100 is used (i.e., each word is represented by a vector of length 100). We utilized pre-trained global vectors BIBREF22 . The training phase is depicted in Figure FIGREF2 .
To make the English sentiment analysis model as generalizable as possible, we first start by training with a large dataset that has product reviews for different categories. Then, using the trained weights from the larger generic dataset, we make the model more specialized for a specific domain. We further train the model with domain-specific English reviews and use this trained model to score reviews that share the same domain from different languages.
Answer: They first start by training with a large dataset that has product reviews for different categories. Then, using the trained weights from the larger generic dataset, they make the model more specialized for a specific domain. They further train the model with domain-specific English reviews and use this trained model to score reviews that share the same domain from different languages.