The Opinion Analysis System

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IMPLEMENTATION OF SYSTEM

The Opinion analysis system consists of seven phases. In the implementation part each phases is defined in detail with regards to the required input, its working and output. The phases are as follows
4.1 OPINION DATA
In this phase the Opinion form was provided to the students of the institute. It consists of various guidelines about the institute so it will help in filling the form quickly. The guidelines provided to the student were regarding college entities about which they have to give Opinion.. The student provided the Opinion and that Opinion is taken as an input to the system. The Opinion forms look like.

Fig: 4.1 Opinion form Opinion from students was collected.
Example:
• Placement is very good.
• Reading …show more content…

The Stanford tagger that uses the oxford dictionary for finding the word tag of the particular word the tagger uses the 10, 00,000 words from the oxford dictionary to trained the datasets. There are total 36 tags from the Peen Tree bank analysis. All the words are tagged within these 36 tag. The system defines the Maxnet tagger method for calling the instance of Stanford tagger i.e English bidirectional tagger and left words tagger merely used for the tagging purpose. By using these methods the word are tag according to the peen tree bank analysis and the list of words are retrieved for further tokenized for …show more content…

The feature identification involves finding the entity or features about which the opinions are expressed in the sentences. The pre-processing step provides with the word along with the tag. The English dictionary part of speech tags the entity or feature word with the Noun tag i.e. NN or Proper Noun i.e. NNP.
The training dataset in this system was observed and based on that the grammar is designed for the system. In the proposed system all noun words and feature descriptive words are identified. So that from this feature identification phase the list of all the important feature words can be retrieved and will be further used for the semantic matching. The training dataset was observed and based on that the tags that are important for feature identification is set. The tags are as follows , , , .
Output of the pre-processing phase, a list of tagged words is use to retrieve the words which follow the grammar accordingly list of all important feature word is prepared. The words in the tag are checked by the grammar if that tag word matches to the specified grammar then that word is

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