【摘要】：微博已經(jīng)成為人們快速分享和傳播信息的平臺(tái)，其特點(diǎn)是全民都可以在微博上隨時(shí)隨地發(fā)布和分享信息。為了實(shí)現(xiàn)基于位置的服務(wù)，如何從分散和多樣化的信息中推測用戶的位置成為了微博時(shí)代面臨的一個(gè)難點(diǎn)和熱點(diǎn)問題。結(jié)合國內(nèi)外現(xiàn)有的位置推測技術(shù)，以已知的位置知識(shí)為前提，針對如何提高不同地理粒度下位置推測的準(zhǔn)確率和解決位置信息的稀疏性問題，本文對基于微博的位置推測技術(shù)進(jìn)行了研究。 首先，為了實(shí)現(xiàn)在城區(qū)和街道級別的粒度下位置推測，提出了一種基于語言模型的微博位置推測方法。充分利用微博中城區(qū)和街道粒度下地理信息的特征，，通過改進(jìn)的本地詞匯提取算法來構(gòu)建基于語言模型的微博位置推測方法。實(shí)驗(yàn)結(jié)果表明，該方法可以實(shí)現(xiàn)一元語言模型和二元語言模型下的城區(qū)級別位置推測，f-measure分別為0.32和0.34。同時(shí)可以實(shí)現(xiàn)城區(qū)和街道粒度下位置推測，且召回率分別達(dá)到了24.9%和16.36%；同時(shí)實(shí)驗(yàn)結(jié)果也表明現(xiàn)有微博位置推測技術(shù)的準(zhǔn)確率和召回率還有待提高，尤其是需要解決位置信息稀疏性的問題。 其次，針對在微博位置信息稀疏性情況下位置推測精度不高的問題，提出了一種基于微博內(nèi)容的用戶位置的推測方法。先從用戶的微博內(nèi)容中提取與地理相關(guān)的本地詞匯，并計(jì)算不同地區(qū)本地詞匯的權(quán)重；然后憑借分詞后的微博內(nèi)容與本地詞匯的匹配程度來對用戶的位置進(jìn)行推測。實(shí)驗(yàn)結(jié)果表明，基于微博內(nèi)容的位置推測方法在省份級別和城市級別上的準(zhǔn)確率分別達(dá)到了68.49%和66.52%，優(yōu)于已有的基于基準(zhǔn)算法、地名詞典和TEDAS的位置推測方法。 最后，為了進(jìn)一步提高位置推測精度，提出了一種基于微博內(nèi)容和互粉好友的用戶位置推測方法。該方法通過將基于微博內(nèi)容位置推測和基于互粉好友位置兩種推測方法相結(jié)合來提高位置推測位置的精度。實(shí)驗(yàn)結(jié)果表明，本方法的推測位置準(zhǔn)確率優(yōu)于基于微博內(nèi)容、互粉好友、基準(zhǔn)算法、地名詞典和TEDAS的位置推測方法；在微博位置信息稀疏的情況下省份級別位置推測精度達(dá)到81.39%，城市級別位置推測精度達(dá)到78.85%。
[Abstract]:Weibo has become a platform for people to share and disseminate information quickly, which is characterized by the ability of the whole people to publish and share information anytime, anywhere on Weibo. In order to realize location-based service, how to speculate the location of users from decentralized and diversified information has become a difficult and hot issue in the Weibo era. Combined with the existing location conjecture technology at home and abroad, and on the premise of known location knowledge, this paper studies the location conjecture technology based on Weibo in order to improve the accuracy of location conjecture under different geographical granularity and solve the problem of sparsity of location information. Firstly, in order to realize location conjecture at the urban and street levels, a Weibo location conjecture method based on language model is proposed. Making full use of the characteristics of geographical information in urban and street granularity in Weibo, an improved local vocabulary extraction algorithm is used to construct Weibo location estimation method based on language model. The experimental results show that the proposed method can realize the urban level location conjecture under the unilanguage model and the binary language model, and the f-measure is 0.32 and 0.34, respectively. At the same time, the location conjecture under urban and street granularity can be realized, and the recall rate is 24.9% and 16.36%, respectively. at the same time, the experimental results also show that the accuracy and recall rate of the existing Weibo location speculation technology still need to be improved, especially the problem of location information sparsity needs to be solved. Secondly, in order to solve the problem that the accuracy of location estimation is not high under the condition of sparsity of Weibo location information, a method of user location estimation based on Weibo content is proposed. Firstly, the local vocabulary related to geography is extracted from the Weibo content of the user, and the weight of the local vocabulary in different regions is calculated, and then the location of the user is deduced by the matching degree between the Weibo content and the local vocabulary after word segmentation. The experimental results show that the accuracy of the location estimation method based on Weibo content at the provincial level and the urban level is 68.49% and 66.52%, respectively, which is superior to the existing location estimation methods based on benchmark algorithm, toponymic dictionary and TEDAS. Finally, in order to further improve the accuracy of location estimation, a user location estimation method based on Weibo content and mutual friends is proposed. In this method, the accuracy of location estimation is improved by combining the two methods based on Weibo content location estimation and mutual powder friend location estimation. The experimental results show that the accuracy of location estimation in this method is better than that based on Weibo content, mutual friends, benchmark algorithm, toponymic dictionary and TEDAS, and the accuracy of location estimation at provincial level and city level is 81.39% and 78.85% respectively when Weibo position information is sparse.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092;TP391.1

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