| Type of Material: | Thesis |
| Title: | Mining Opinions about Traffic Status in Tweets using Sentiment Analysis |
| Researcher: | BOOPALAN, K |
| Guide: | NALINI, C |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2018 |
| Language: | English |
| Subject: | Computer Science | Computer Science Theory and Methods | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2018; D13CS505 |
| Fulltext: | Shodhganga |
| 000 | 00000ntm a2200000ua 4500 | |
| 001 | 454743 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-18 16:45:08 | |
| 008 | __ | 240918t2018||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_454743 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aBOOPALAN, K|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein |
| 245 | __ | |aMining Opinions about Traffic Status in Tweets using Sentiment Analysis |
| 260 | __ | |aChennai|bBharath University, Chennai|c2018 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2018|oD13CS505 |
| 520 | __ | |aText Analytics is the most promising field in information technology the past decade. Most of the organizations use text analytics to uncover meaningful information from unstructured text because considering Natural language processing techniques are highly challenging. They often cause many problems due to the inconsistency in syntax and semantics.. This research work focuses on the importance of text analytics in the field of traffic analysis and evaluates the performance of various text classification algorithms. This thesis proposes experiments, demonstrates and evaluates the concept of mining opinions about traffic in tweet messages using sentiment analysis. Experimentation involves discussion and comparison of ensemble classifiers over labeled tweets. A maximum of 1500 tweets per day were called for on four different days and almost 5000 tweets have been collected in all. In this research work, the research I have taken up the task of extracting public opinion on traffic conditions from the tweets the |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Theory and Methods |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aNALINI, C|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/314282|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.