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情感分析技能,分析用户评论情感倾向,评估痛点严重程度,提取关键词和主题
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Basé sur la classification professionnelle SOC
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| name | sentiment-analysis |
| description | 情感分析技能,分析用户评论情感倾向,评估痛点严重程度,提取关键词和主题 |
通过分析用户评论和讨论的情感倾向,量化用户满意度和痛点严重程度。
正面关键词:
love, great, awesome, perfect, excellent, amazing,
fantastic, wonderful, best, recommend, helpful,
easy, simple, fast, smooth, reliable
负面关键词:
hate, terrible, awful, worst, horrible, useless,
disappointing, frustrating, annoying, slow, buggy,
crash, broken, waste, regret, avoid
强度词:
very, extremely, absolutely, completely, totally,
really, so, too, incredibly, unbelievably
SQL 实现:
-- 简单情感分类
SELECT
id,
quote,
CASE
WHEN LOWER(quote) LIKE '%love%' OR LOWER(quote) LIKE '%great%'
OR LOWER(quote) LIKE '%awesome%' OR LOWER(quote) LIKE '%perfect%'
THEN 'positive'
WHEN LOWER(quote) LIKE '%hate%' OR LOWER(quote) LIKE '%terrible%'
OR LOWER(quote) LIKE '%awful%' OR LOWER(quote) LIKE '%worst%'
THEN 'negative'
ELSE 'neutral'
END as sentiment,
CASE
WHEN LOWER(quote) LIKE '%very%' OR LOWER(quote) LIKE '%extremely%'
THEN 'high'
ELSE 'medium'
END as intensity
FROM user_quotes;
情感评分 (-5 到 +5):
计算方法:
1. 统计正面关键词数量 (P)
2. 统计负面关键词数量 (N)
3. 统计强度词数量 (I)
4. 评分 = (P - N) × (1 + I × 0.5)
示例:
"I absolutely love this app!"
- P = 1 (love)
- N = 0
- I = 1 (absolutely)
- 评分 = (1 - 0) × (1 + 1 × 0.5) = 1.5
"This is the worst app ever!"
- P = 0
- N = 1 (worst)
- I = 0
- 评分 = (0 - 1) × (1 + 0) = -1
| 痛点 | 情感强度 | 频率 | 持续性 | 影响范围 | 总分 | 等级 |
|------|---------|------|--------|---------|------|------|
| 电池焦虑 | -4.2 | 45 | 6个月 | 广泛 | 18 | 严重 |
| 存储满 | -3.8 | 32 | 12个月 | 广泛 | 16 | 严重 |
| 通知多 | -2.5 | 18 | 3个月 | 中等 | 8 | 中等 |
评分标准:
-- 痛点严重程度分析
SELECT
category,
COUNT(*) as frequency,
AVG(sentiment_score) as avg_sentiment,
DATEDIFF(MAX(created_at), MIN(created_at)) as duration_days,
COUNT(DISTINCT source) as source_diversity,
-- 综合评分
(COUNT(*) * 0.3 +
ABS(AVG(sentiment_score)) * 2 +
DATEDIFF(MAX(created_at), MIN(created_at)) / 30 * 0.5 +
COUNT(DISTINCT source) * 0.5) as severity_score
FROM user_quotes
WHERE sentiment_score < 0 -- 只看负面
GROUP BY category
ORDER BY severity_score DESC;
方法:
1. 分词 (去除停用词)
2. 统计词频
3. 按频率排序
4. 提取 Top 20-30
停用词列表:
the, a, an, and, or, but, in, on, at, to, for,
of, with, by, from, is, are, was, were, be, been
SQL 实现 (简化版):
-- 提取包含特定词的评论数
SELECT
'battery' as keyword,
COUNT(*) as mention_count,
COUNT(*) * 100.0 / (SELECT COUNT(*) FROM user_quotes) as percentage
FROM user_quotes
WHERE LOWER(quote) LIKE '%battery%'
UNION ALL
SELECT
'storage' as keyword,
COUNT(*) as mention_count,
COUNT(*) * 100.0 / (SELECT COUNT(*) FROM user_quotes) as percentage
FROM user_quotes
WHERE LOWER(quote) LIKE '%storage%';
常见主题:
主题分类:
SELECT
CASE
WHEN LOWER(quote) LIKE '%slow%' OR LOWER(quote) LIKE '%lag%'
OR LOWER(quote) LIKE '%crash%'
THEN 'performance'
WHEN LOWER(quote) LIKE '%missing%' OR LOWER(quote) LIKE '%need%'
OR LOWER(quote) LIKE '%want%'
THEN 'feature_request'
WHEN LOWER(quote) LIKE '%confusing%' OR LOWER(quote) LIKE '%complicated%'
THEN 'ux_issue'
WHEN LOWER(quote) LIKE '%expensive%' OR LOWER(quote) LIKE '%price%'
THEN 'pricing'
WHEN LOWER(quote) LIKE '%bug%' OR LOWER(quote) LIKE '%broken%'
THEN 'bug'
ELSE 'other'
END as theme,
COUNT(*) as count
FROM user_quotes
GROUP BY theme
ORDER BY count DESC;
| 竞品 | 平均情感 | 正面% | 中性% | 负面% | 主要抱怨 |
|------|---------|-------|-------|-------|---------|
| A | +1.2 | 45% | 35% | 20% | 价格贵 |
| B | -0.5 | 25% | 40% | 35% | 功能少 |
| C | +2.1 | 60% | 30% | 10% | 无 |
SQL 查询:
SELECT
competitor_name,
AVG(sentiment_score) as avg_sentiment,
SUM(CASE WHEN sentiment = 'positive' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) as positive_pct,
SUM(CASE WHEN sentiment = 'neutral' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) as neutral_pct,
SUM(CASE WHEN sentiment = 'negative' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) as negative_pct
FROM app_reviews
GROUP BY competitor_name
ORDER BY avg_sentiment DESC;
-- 月度情感趋势
SELECT
strftime('%Y-%m', created_at) as month,
AVG(sentiment_score) as avg_sentiment,
COUNT(*) as review_count
FROM user_quotes
GROUP BY month
ORDER BY month;
可视化:
月份: 01 02 03 04 05 06
情感: +1.2 +0.8 -0.5 -1.2 -1.5 -1.8 (趋势恶化)
## iOS 18 发布对情感的影响
**发布前 (2024-08)**:
- 平均情感: +0.5
- 负面评论: 25%
**发布后 (2024-10)**:
- 平均情感: -1.2
- 负面评论: 45%
**变化**: 情感显著恶化,负面评论增加 80%
# [主题] 情感分析报告
## 概况
- 分析时间: [时间范围]
- 数据量: X 条评论/讨论
- 数据来源: [列表]
## 整体情感分布
| 情感 | 数量 | 占比 |
|------|------|------|
| 正面 | X | XX% |
| 中性 | X | XX% |
| 负面 | X | XX% |
**平均情感评分**: X.X (-5 到 +5)
## 痛点严重程度排名
1. [痛点1] - 严重程度: XX/20
2. [痛点2] - 严重程度: XX/20
3. [痛点3] - 严重程度: XX/20
## 高频关键词
### 正面
1. [词1] - 提及 X 次
2. [词2] - 提及 X 次
### 负面
1. [词1] - 提及 X 次
2. [词2] - 提及 X 次
## 主题分布
| 主题 | 数量 | 占比 | 平均情感 |
|------|------|------|---------|
| [主题1] | X | XX% | X.X |
## 竞品对比
[竞品情感对比表]
## 时间趋势
[情感趋势图]
## 典型评论样本
### 最正面
> "[引用]"
### 最负面
> "[引用]"
## 洞察与建议
1. [基于情感分析的洞察]
2. [建议]
-- 情感分析结果表
CREATE TABLE sentiment_analysis (
id INTEGER PRIMARY KEY,
quote_id INTEGER,
sentiment TEXT, -- positive/neutral/negative
sentiment_score REAL, -- -5 to +5
intensity TEXT, -- low/medium/high
theme TEXT,
keywords TEXT, -- JSON array
analyzed_at TEXT,
FOREIGN KEY (quote_id) REFERENCES user_quotes(id)
);
-- 关键词频率表
CREATE TABLE keyword_frequency (
id INTEGER PRIMARY KEY,
keyword TEXT,
category TEXT,
frequency INTEGER,
sentiment_association TEXT, -- positive/negative
last_updated TEXT
);