步骤 3：整理（与清洗）文本

R 中的情感分析

Ted Kwartler

Data Dude

开始动手！

初始目标：使用 polarity() 为文本划分待分析的小节。

pos_comments <- subset(bos_reviews$comments, 
                       bos_reviews$polarity > 0)
neg_comments <- subset(bos_reviews$comments, 
                       bos_reviews$polarity < 0)

pos_terms <- paste(pos_comments, collapse = " ")
neg_terms <- paste(neg_comments, collapse = " ")

进一步整理

目标：用整洁化的租房评论生成整洁格式的极性评分。

library(tidytext)
library(dplyr)

tidy_reviews <- bos_reviews %>% 
    unnest_tokens(word, comments)

tidy_reviews <- tidy_reviews %>% 
    group_by(id) %>%
    mutate(original_word_order = seq_along(word))

整洁文本的极性评分

回顾：sentiments 中的 "bing" 词典将词标注为正面或负面。

library(tidytext)
library(tidyr)
library(dplyr)

bing <- sentiments %>% 
    filter(lexicon == "bing")

pos_neg <- tidy_reviews %>% 
    inner_join(bing) %>%
    count(sentiment) %>%
    pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) %>%
    mutate(polarity = positive - negative)

让我们来练习！

R 中的情感分析