**4. Results and discussions**

For experiments, a random public user Twitter dataset and real-time data using the API of Twitter is complete. The Twitter tweets containing the keywords "basket," "pencil," "work," "enter," and "formal" from the public domain are taking as the standard bag-of-words approach. Used this dataset for classification and collected 300 documents in each of the public domains.

For the classification of tweets, the true +ves, true -ves, false +ves, and false -ves constraints are utilized to equate the consequences of the classifier under the test with investigation techniques, which is illustrating in **Figure 7**.

The relations between TP, FP, FN, and TN are:



**Figure 7.** *Classification matrix model for metric analysis.*

**Figure 8.** *Accuracy comparison of a classified tweet.*

*Knowledge Extraction from Open Data Repository DOI: http://dx.doi.org/10.5772/intechopen.100234*

**Figure 9.** *Comparison of different algorithms for measured values.*

**Figure 8** shows the accuracy of classifying a user query tweet in the users defined the recommended system. The highest accuracy is achieved through the proposed work, with an incredible number of word phrases, depending on the content of the user query and compared with Naive Bayes classifier (NB) [41].

**Figure 9** compares the proposed system with different dataset approaches in terms of F1-score and the exact match. Because of the phrase-based content mining is made and tweets analysis is made accurately on two other datasets of different methods [42, 43] and proposed.

#### **5. Conclusions**

Social media connects connected individuals in numerous ways. For instance, online people can interact, communicate, collaborate, and socialize, showing new types of integration and interaction that were difficult to imagine only a short period before. Online communities also play a significant role in entrepreneurship, influence company feelings, and concepts and create many opportunities for unique investigation of person and team performance. In this chapter, a user querycentered recommendation system is deliberate to improve user-query analysis and tweets analysis for Twitter tweets. The proposed and implemented query categorization gives a satisfactory exact match performance in tweets categorizing. The content model is the most important model for the majority of tweets. Apart from finding the tweets, the phrases are identified and located in accuracy performance metric. As discussed, content integration is helpful for tweets match retrieval. For the given user tweet query, if the aim is to retrieve similar tweets from the public repository, retrieving the keywords is improved with the use of words and phrases.

Also, a novel algorithm based on content detection is using to extract the tweets using the bags-of-word method. Using the tweet's knowledge weights the proposed recommendation system avoids the dissimilar tweet's pattern identification problem. The above said three parameters are complete, which indicates that the proposed approach produces better accuracy results than the other methods.
