The goal is as follows: I have a big article and I want to define the sentiment of the particular word. For example, the article describes pros and cons of bikes and cars and I want to find the sentiment of the word car.
In such an example I cannot use document-level SA as the article itself can be positive while the car was mentioned in a negative way.
So, I studied papers related to aspect-based sentiment analysis, but my constait is absence of data for training NNs. Hence, I concentrated on the approaches that basically do not involve training process. One of my attemts was to build sentiment analysis tool using word2vec and K-Means so that each cluster corresponds to one of three sentiments (pos, neg and neu).
It actually worked great but I found that for some reason one word can be at two clusters at the same time. Plus it generally goes not give sentiment for specific keyword but for all aspects found in the text.
Another problem is that basically cannot test the correctness of the output if only not to read the text by myself and check whether the keyword belonged to the correct cluster or not.
So I came to the decision to make summarization of the article first and then applying sentiment analysis (like sentiwordnet or similar).
Are there ways to improve word2vec+KMeans approach? Is it even worse improving?
Is it a good idea to go through text summarization before sentiment analysis?
Is there better way to find sentiment of the particular word without training process (due to no training data and small amount of unlabeled data)?