Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment Analysis [2]: the process of understanding if a given text is talking positively or negatively about a given subject, aiming for brand monitoring.
Sentiment analysis has been investigated mainly at three levels:
Document level:
The task at this level is to classify whether a whole opinion document expresses a positive or negative sentiment (Pang, Lee and Vaithyanathan, 2002; Turney, 2002). For example, given a product review, the system determines whether the review expresses an overall positive or negative opinion about the product. This task is commonly known as document-level sentiment classification. This level of analysis assumes that each document expresses opinions on a single entity (e.g., a single product). Thus, it is not applicable to documents which evaluate or compare multiple entities.
Sentence level:
The task at this level goes to the sentences and determines whether each sentence expressed a positive, negative, or neutral opinion. Neutral usually means no opinion. This level of analysis is closely related to subjectivity classification (Wiebe, Bruce and O'Hara, 1999), which distinguishes sentences (called objective sentences) that express factual information from sentences (called subjective sentences) that express subjective views and opinions. However, we should note that subjectivity is not equivalent to sentiment as many objective sentences can imply opinions, e.g., “We bought the car last month and the windshield wiper has fallen off.” Researchers have also analyzed clauses (Wilson, Wiebe and Hwa, 2004), but the clause level is still not enough, e.g., “Apple is doing very well in this lousy economy.”
Entity and Aspect level:
Both the document level and the sentence level analyses do not discover what exactly people liked and did not like. Aspect level performs finer-grained analysis. Aspect level was earlier called feature level (feature-based opinion mining and summarization) (Hu and Liu, 2004). Instead of looking at language constructs (documents, paragraphs, sentences, clauses or phrases), aspect level directly looks at the opinion itself. It is based on the idea that an opinion consists of a sentiment (positive or negative) and a target (of opinion). An opinion without its target being identified is of limited use. Realizing the importance of opinion targets also helps us understand the sentiment analysis problem better.
Long Short Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN), capable of learning long-term dependencies. These long-term dependencies have a great influence on the meaning and overall polarity of a document. Long short-term memory networks (LSTM) address this long-term dependency problem by introducing a memory into the network. It was first introduced by Hochreiter & Schmidhuber.
The LSTM architecture has a range of repeated modules for each time step as in a standard RNN. At each time step, the output of the module is controlled by a set of gates, as a function of the old hidden state ℎ𝑡−1 and the input at the current time step 𝑥𝑡 : the forget gate 𝑓𝑡, the input gate 𝑖𝑡 , and the output gate 𝑂𝑡 . These gates collectively decide how to update the current memory cell 𝐶𝑡 and the current hidden state ℎ𝑡 . The LSTM transition functions are defined as follows:
𝑖𝑡=(𝑊𝑖[ℎ𝑡−1,𝑥𝑡]+𝑏𝑖)
𝐶´𝑡=𝑡𝑎𝑛ℎ(𝑊𝑐[ℎ𝑡−1,𝑥𝑡]+𝑏𝐶)
𝑓𝑡=(𝑊𝑓[ℎ𝑡−1,𝑥𝑡]+𝑏𝑓)
𝑂𝑡=(𝑊𝑜[ℎ𝑡−1,𝑥𝑡]+𝑏𝑜)
𝐶𝑡= 𝑓𝑡∗𝐶𝑡−1+𝑖𝑡∗𝐶´𝑡
Here 𝜎 is logistic sigmoid function that has an output in 𝑛 [0,1] ,tanh denotes the hyperbolic tangent function that has an output ℎ 𝑖𝑛 [−1,1], and ∗ denotes the pointwise multiplication.
As I mentioned before to deal with Deep learning we have to map sentence to vector of number. In this work inspired by [3] Doc2vec as the embedding used for extracting information context. The Doc2vec is an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts such as sentences, paragraphs, and documents.