临平区旅游场所文化价值评价数据

场所管理者可依据文化价值评分数据针对临平区的不同旅游场所进行文化价值优先级排序，合理分配资源投入和资源维护。利用文化价值评价数据，教育机构可以与高评价场所合作，开发文旅融合教育项目，提高公众对本地文化的认识和参与度。根据场所的文化价值评价数据，旅游服务商可以设计主题文化旅游路线，将高评分场所作为旅游路线的重点选择。步骤1，数据收集和预处理：从公司文化保障卡服务系统中自动抽取临平区域旅游场所相关数据，包括场馆ID、历史背景介绍、文化活动频次、活动评价。通过数据清洗去除无效或错误记录，确保数据质量。
步骤2，历史背景评分，使用TF-IDF模型提取历史背景介绍中的关键词和短语，利用命名实体识别（NER）技术识别文本中的地点、人物、时间等实体，使用自然语言处理（NLP）技术，基于预定义的规则（如提及历史事件、著名人物等）为文本打分。
步骤3，文化活动频次评分计算采用线性公示，设定每月一次次为基准频次，得60分，每增加一次得20分。
步骤4，评价情感分数计算，使用预训练的BERT情感分析模型（一种基于深度学习的文本分析工具）对活动评价文本进行情感倾向分类，赋值情感分数。
步骤5，文化价值评分计算，使用加权求和的方法计算每个场馆的文化价值评分，公式为：文化价值评分 = α×历史背景评分+β×文化活动频次评分+γ×用户评价情感分数。α、β、γ 是权重因子，根据实际重要性分配和调整。

Managers of tourist venues can prioritize cultural values of various tourist sites in Linping District based on cultural value scoring data, to rationally allocate resource investment and maintenance efforts. Using cultural value evaluation data, educational institutions can cooperate with highly-rated venues to develop education programs integrating culture and tourism, enhancing public awareness of and participation in local culture. Based on the cultural value evaluation data of venues, tourism service providers can design themed cultural tourism routes, prioritizing high-rated sites as key stops.

Step 1: Data Collection and Preprocessing
Automatically extract data related to tourist venues in Linping District from the company's Cultural Security Card Service System, including venue ID, historical background introduction, frequency of cultural activities, and activity reviews. Perform data cleaning to remove invalid or erroneous records and ensure data quality.

Step 2: Historical Background Scoring
Use the TF-IDF model to extract keywords and phrases from the historical background introduction text. Employ Named Entity Recognition (NER) technology to identify entities such as locations, characters, and time in the text. Then utilize Natural Language Processing (NLP) technologies to score the text based on pre-defined rules (e.g., mentions of historical events, famous figures, etc.).

Step 3: Cultural Activity Frequency Scoring
The scoring for cultural activity frequency is calculated using a linear formula, with the benchmark frequency set as once per month corresponding to a score of 60, and an additional 20 points for each extra occurrence.

Step 4: Sentiment Score Calculation
Use the pre-trained BERT sentiment analysis model (a deep learning-based text analysis tool) to classify the sentiment orientation of activity review texts and assign sentiment scores.

Step 5: Cultural Value Score Calculation
Calculate the cultural value score for each venue using the weighted summation method, with the formula: Cultural Value Score = α × Historical Background Score + β × Cultural Activity Frequency Score + γ × User Review Sentiment Score. Here, α, β, and γ are weight factors that can be allocated and adjusted based on actual importance.

搜集汇总

数据集介绍

特点

该数据集提供了临平区旅游场所的文化价值评价数据，包含538条记录，涵盖场馆ID、历史背景、文化活动频次及评价等多个维度。通过特定的算法规则，如TF-IDF模型、BERT情感分析模型等，对数据进行处理，最终得出文化价值评分，适用于场所管理、教育合作及旅游路线设计等应用场景。