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EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

Mining the opinion relations between opinion targets and opinion words was the key to collective extraction. To this end, the mostadopted techniques have been nearest-neighbor rules and syntactic patterns.

To improve the performance of these methods, we can specially design exquisite, high-precision patterns. However, with an increase in corpus size, this strategy is likely to miss more items and has lower recall.

PROPOSED CONCEPT:-

We propose a method based on a monolingualWord alignment model (WAM). An opinion target can find its corresponding modifier through word alignment.

In addition, the WAM can integrate several intuitive factors, such as word co-occurrence frequencies and word positions, into a unified model for indicating the opinion relations among words. Thus, we expect to obtain more precise results on opinion relation identification.

EXISTING TECHNIQUE:-

Bootstrapping Framework.

PROPOSED TECHNIQUE:-

Word Alignment Model(WAM).

TECHNIQUE DEFNITION:-

It has the problem of error propagation. If some errors are extracted by iteration, they would not be filtered out in subsequent iterations. As a result, more errors are accumulated iteratively.

TECHNIQUE DEFNITION:-

The WAM does not constrain identifying modified relations to a limited window; therefore, it can capture more complex relations, such as long-span modified relations.

DRAWBACKS:-

If some errors extracted by iteration, they would not be filtered out in subsequent iterations.

In existing system improve the performance.

ADVANTAGES:-

It has proved to be effective for opinion target extraction.

In propose system is also improving the performance and demonstrate the WAM’s effectiveness.