本文關(guān)鍵詞： 北京市 旅游人群 行為特征 情感算法　出處：《首都經(jīng)濟(jì)貿(mào)易大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：網(wǎng)絡(luò)旅游平臺及大數(shù)據(jù)技術(shù)迅猛發(fā)展,旅游平臺需借助網(wǎng)絡(luò)爬蟲以及文本處理等技術(shù)獲知如何完善平臺。本報(bào)告研究目標(biāo)就是運(yùn)用大數(shù)據(jù)文本分析技術(shù)充分挖掘來北京市旅游的人群行為特征如出行時(shí)間、客源地結(jié)構(gòu)、出行方式及結(jié)伴方式等,同時(shí)獲取北京市最具熱度的前100個(gè)景點(diǎn)所對應(yīng)的文本評論數(shù)據(jù),組成具有321550條旅游情感評價(jià)的語料庫,研究不同行為的旅游人群情感分值差異,從而為完善平臺建設(shè)提供建議。本報(bào)告主要運(yùn)用以下四種研究方法:第一,文本聚類。將游客評論進(jìn)行分詞并轉(zhuǎn)換成向量,運(yùn)用K-Means進(jìn)行聚類,得出旅游人群評價(jià)維度。第二,情感分析法。建立情感詞典運(yùn)用情感分析算法來進(jìn)行文本維度屬性情感詞的匹配,實(shí)現(xiàn)情感分值的量化處理。第三,內(nèi)容分析法。通過對語料庫中景點(diǎn)評論文本進(jìn)行詞頻分析,提取與游客情感相關(guān)的高頻詞,細(xì)化旅游人群情感評價(jià)。第四,對比分析法。對比不同行為的旅游人群情感分值以及評價(jià)詞語差異。對旅游人群數(shù)據(jù)進(jìn)行綜合研究得出主要結(jié)論:第一,北京對距離近的地區(qū)產(chǎn)生更大游玩吸引。第二,出行時(shí)間對于公園樂園、古跡遺址以及自然景觀類景點(diǎn)的評分影響比較大,情感分值在一年中呈現(xiàn)兩端月份低,中間月份高的現(xiàn)象。第三,結(jié)伴方式有不同。單獨(dú)出游、家庭出游、朋友出游、情侶出游以及商務(wù)旅行對于景點(diǎn)類型的喜好以及評分有差異。第四,出行方式顯個(gè)性。選擇跟團(tuán)游、自由行及自駕游人群畫像有差異,產(chǎn)品喜好與情感分值評價(jià)不同。網(wǎng)絡(luò)旅游平臺可通過以下來進(jìn)行優(yōu)化:第一,構(gòu)建多元評價(jià)維度;第二,精確定位推薦旅游產(chǎn)品時(shí)間;第三,細(xì)化營銷推薦人群;第四,優(yōu)化產(chǎn)品特色服務(wù);第五,加大基礎(chǔ)設(shè)施投入。
[Abstract]:Internet tourism platform and big data technology are developing rapidly. Tourism platform needs to know how to improve the platform by means of web crawler and text processing technology. The goal of this report is to fully excavate the behavior characteristics of Beijing tourism population by using big data text analysis technology, such as travel time. Between. At the same time, we obtain the text review data of the top 100 scenic spots with the most heat in Beijing, and form a corpus of 321550 tourism emotion evaluation. The study of different behavior of tourism groups emotional score differences, thus providing suggestions for improving the platform construction. This report mainly uses the following four research methods: first. Text clustering. The tourists' comments are partitioned and converted into vectors, and K-Means are used to cluster to get the tourist crowd evaluation dimension. Second. Affective analysis. The establishment of emotion dictionary using emotional analysis algorithm to match the text dimension attributes emotional words, to achieve the quantification of emotional score processing. Third. Content analysis. Through the word frequency analysis of the comment text of scenic spots in the corpus, extract the high-frequency words related to the tourists' emotion, refine the emotional evaluation of the tourist crowd. 4th. Contrastive analysis. Compare the different behavior of tourism groups emotional scores and evaluation of the differences in words. The comprehensive study of the tourist population data draw the main conclusions: first. Beijing has a greater attraction to nearby areas. Second, travel time has a greater impact on park parks, historic sites and natural landscape sites, emotional scores in the year at both ends of the month low. The phenomenon of high in the middle month. Third, there are different ways of getting together. There are differences in the preference and score of individual travel, family trip, friend trip, couple trip and business travel for the type of scenic spot. 4th. Travel style shows personality. Choose with group tour, free travel and self-driving tour crowd portrait differences, product preferences and emotional evaluation is different. Internet tourism platform can be optimized through the following: first. Constructing multiple evaluation dimension; Second, the time of recommending tourism products; Third, refine the marketing recommendation crowd; 4th, optimize product characteristic service; 5th, increase infrastructure investment.
【學(xué)位授予單位】：首都經(jīng)濟(jì)貿(mào)易大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F592.7

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