<p>You are basically on the right track. I would try and apply classifier with features you already have and see how well it will work, before doing anything else.  </p>

<p>Actually best way to improve your work is to google for subjectivity classification papers and read them (there are a quite a <a href="https://scholar.google.ru/scholar?as_ylo=2011&amp;q=subjectivity%20classification&amp;hl=en&amp;as_sdt=0,5" rel="nofollow">number of them</a>). For example <a href="http://arxiv.org/ftp/arxiv/papers/1312/1312.6962.pdf" rel="nofollow">this one</a> lists typical features for this task.</p>

<p>And yes Chi-squared can be used to construct dictionaries for text classification (other commonly used methods are TD*IDF, pointwise mutal information and LDA)</p>

<p>Also, recently new neural network-based methods for text classification such as <a href="http://arxiv.org/pdf/1405.4053v2.pdf" rel="nofollow">paragraph vector</a> and <a href="http://arxiv.org/pdf/1406.3830v1.pdf" rel="nofollow">dynamic convolutional neural networks with k-max pooling</a> demonstrated state-of-the-art results on sentiment analysis, thus they should probably be good for subjectivity classification as well.</p>
Features Vectors to build classifier to detect subjectivity
<p>I am trying to build a classifier to detect subjectivity. I have text files tagged with subjective and objective . I am little lost with the concept of features creation from this data. I have found the lexicon of the subjective and objective tag. One thing that I can do is to create a feature of having words present in respective dictionary. Maybe the count of words present in subjective and objective dictionary. After that I intend to use naive bayes or SVM to develop the model</p>

<p>My problem is as follow</p>

<ol>
<li>Is my approach correct ?</li>
<li>Can I create more features ? If possible suggest some or point me to some paper or link</li>
<li>Can I do some test like chi -sq etc to identify effective words from the dictionary ?</li>
</ol>